Colony contrast gathering

ABSTRACT

An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/681,333 filed Nov. 12, 2019, which is allowed, which application is acontinuation of U.S. application Ser. No. 15/567,775 filed Oct. 19,2017, now U.S. Pat. No. 10,521,910, issued Dec. 31, 2019 and is anational phase entry under 35 U.S.C. § 371 of International ApplicationNo. PCT/US/2016/028913 filed Apr. 22, 2016 published in English, whichclaims priority from U.S. Provisional Application No. 62/151,681, filedApr. 23, 2015, and U.S. Provisional Application No. 62/318,483, filedApr. 5, 2016, all of which are incorporated herein by reference.

BACKGROUND OF THE INVENTION

There is increased focus on digital imagery of culture plates fordetection of microbial growth. Techniques for imaging plates fordetecting microbial growth are described in PCT Publication No.WO2015/114121, the entirety of which is incorporated by referenceherein. Using such techniques, laboratory staff is no longer required toread plates by direct visual inspection but can use high quality digitalimages for plate inspection. Shifting laboratory workflow anddecision-making to examination of digital images of culture plates canalso improve efficiency. Images can be marked by an operator for furtherwork-up by either the operator or another person with the appropriateskills. Additional images may also be taken and used to guide secondaryprocesses.

Detection of colonies, colony enumeration, colony populationdifferentiation and colony identification define the objectives for amodern microbiology imaging system. Having these objectives realized asearly as possible achieves the goals of delivering results to a patientquickly and providing such results and analysis economically. Automatinglaboratory workflow and decision-making can improve the speed and costat which these goals may be achieved.

Although significant progress has been made regarding imagingtechnologies for detecting evidence of microbial growth, it is stillsought to extend such imaging technologies to support an automatedworkflow. Apparatus and methods for inspecting culture plates forindications of microbial growth are difficult to automate, due in partto the highly visual nature of plate inspection. In this regard, it isdesirable to develop techniques that may automatically interpret cultureplate images and determine the next steps to be performed (e.g.,identification of colonies, susceptibility testing, etc.) based on theautomated interpretation.

For example, identifying and distinguishing colonies in a plated culturecan be difficult, especially when the colonies are of different size andshape and are touching each other. These problems are exacerbated whengrowth has already reached confluence in some regions of the plate. Forthese reasons, it is preferable, if possible, to identify colonies anddetermine growth early in the process. However, time for incubation isstill needed to allow for at least some growth of the colonies. Thus, onthe one hand, the longer that colonies are allowed to grow, the morethey begin to contrast with their background and each other, and theeasier it becomes to identify them. Yet, on the other hand, if thecolonies are allowed to grow too long and they begin to fill the plateand/or touch one another, it becomes more difficult to contrast themfrom their background and from one another. If one were able to detectcolonies at an incubation time when the colonies were still small enoughto be isolated from one another—despite relatively poor contrast—thisproblem could be minimized or even resolved.

BRIEF SUMMARY OF THE INVENTION

An aspect of the present disclosure is directed to an automated methodfor evaluating microbial growth on plated media, comprising: providing aculture media inoculated with a biological sample disposed in acontainer that is substantially optically transparent; incubating theinoculated culture media in an incubator; placing the transparentcontainer carrying the inoculated culture media in the digital imagingapparatus; obtaining a first digital image of the inoculated culturemedia at a first time (to), the first digital image having a pluralityof pixels; determining coordinates of the pixels in the first digitalimage relative to the container carrying the inoculated culture media;removing the container carrying the inoculated culture media from thedigital imaging apparatus and placing the inoculated culture media inthe incubator for further incubation; after further incubation, placingthe container carrying the inoculated culture media in the digitalimaging apparatus; obtaining a second digital image of the inoculatedculture media at a second time (tx), the second digital image having aplurality of pixels; aligning the first digital image with the seconddigital image, such that the coordinates of a pixel in the seconddigital image correspond to the coordinates of a corresponding pixel inthe first digital image; comparing the pixels of the second digitalimage with corresponding pixels of the first digital image; identifyingpixels that changed between the first digital image and the seconddigital image, wherein the pixels that have not changed between thefirst digital image and the second digital image are indicative ofbackground; determining which of the identified pixels in the seconddigital image have a predetermined level of threshold contrast with thepixels indicative of background; identifying one or more objects in thesecond digital image, each object consisting of pixels that meet saidlevel of threshold contrast and that are not separated from each otherby background pixels; for at least one of the identified objects,determining a morphology of the object from the pixels of the object;from the morphology of the object, determining whether the object is acolony candidate; and providing to memory the coordinates of the pixelsassociated with the object.

In some examples, the method may further comprise obtaining a pluralityof first digital images at the first time according to a predeterminedseries of illumination conditions, wherein each of the first digitalimages is obtained under a different illumination condition, eachillumination condition comprising a specified orientation of theoptically transparent container carrying the inoculated culture mediarelative to an illumination source, and a specified background color onwhich the optically transparent container is placed in the imageacquisition device. The specified orientations may comprise: theillumination source directed downward toward the top of the opticallytransparent container carrying the inoculated culture media; theillumination source directed upward toward the bottom of the opticallytransparent container carrying the inoculated culture media; and theillumination source directed toward a side of the optically transparentcontainer carrying the inoculated culture media. For the specified topand side orientations, the specified background color may be black. Forthe specified bottom orientation, the specified background color may bewhite. The illumination conditions may further comprise a specifiedillumination spectrum comprising: an illumination source emitting redwavelengths; an illumination source emitting green wavelengths; and anillumination source emitting blue wavelengths.

In some examples, the method may further comprise obtaining objectfeatures from the pixel information associated with the object, theobject features comprising at least one of object shape, object size,object edge and object color, wherein the morphology of the object isdetermined based on the object features. Object color may be determinedfrom spectral features of the pixels associated with the object.Spectral features may be selected from the group consisting of pixelcolor, hue, luminance and chrominance. Background feature informationmay also be obtained. Background feature information may comprise mediatype and media color, and the morphology of the object may be determinedbased further on the background feature information. The object featuresand background feature information may be compared with other objectfeatures and other background feature information stored in the memory,and a type of microorganism may be determined based on the objectfeatures and background feature information.

In some examples, aligning the first digital image with the seconddigital image may comprise assigning polar coordinates to pixels of eachof the first and second digital images such that the polar coordinatesof a pixel in the second digital image are the same as the polarcoordinates of a corresponding pixel in the first digital image. Also,in some examples, a plurality of culture media may be provided,inoculated with a biological sample, disposed in one or more opticallytransparent containers, incubated in an incubator, and placed in theimage acquisition device at the same time frame of bacterial growth,whereby first and second digital images are obtained for each culturemedia. Also, in some examples, the method performed by the executedinstructions may further comprise identifying pixel information in thefirst digital image that is evidence of condensation on the opticallytransparent container or plated media, and subtracting the pixelinformation attributed to condensation from the image. Pixel informationthat is evidence of condensation may be identified in a digital imagefor which the illumination source was directed upward toward the bottomof the optically transparent container, and wherein pixel informationfor those pixels having an optical density below a predeterminedthreshold value is subtracted from the image.

In some examples, the method may further comprise: identifying pixelinformation in either of the first and second digital images that isevidence of dust; and subtracting the pixel information attributed todust from the image. Pixel information that is evidence of dust may beidentified in a digital image for which: the optically transparentcontainer contains white culture media, the illumination source isdirected downward toward the top of the optically transparent containerand the background color is black; the optically transparent containercontains colored or dark culture media, the illumination source isdirected downward toward the top of the optically transparent containerand the background color is white; or the illumination source isdirected upward toward the bottom of the optically transparentcontainer. The dust may be dust on any of the culture media, on theoptically transparent container, or an optical component of the imageacquisition device.

In some examples, the method may further comprise: obtaining a thirddigital image of the inoculated media wherein the third digital image isobtained at a time between the time at which the first digital image isacquired and the time and which the second digital image is acquired,wherein the inoculated culture media is removed from the imageacquisition device and placed in the incubator between the acquisitionof the first and second digital images and between the acquisition ofthe second and third digital images; aligning the third digital imagewith the first digital image, such that the coordinates of a pixel inthe third digital image are the same as the coordinates of acorresponding pixel in the first digital image; comparing the pixels ofthe third and second digital images with one another; and identifyingpixels that changed between the third and second digital images. Theincubation time between the first and third digital images may be equalto the incubation time between the third and second digital images. Atleast one of the identified objects may be associated with theidentified pixels that changed between the third and second digitalimages.

In some examples, the method may further comprise: obtaining a thirddigital image of the inoculated media wherein the third digital image isobtained after the time at which the second digital image is obtained,wherein the inoculated culture media is removed from the imageacquisition device and placed in the incubator between the time at whichthe second and third digital images are obtained; aligning the thirddigital image with the first and second digital images, such that thecoordinates of a pixel in the third digital image are the same as thecoordinates of a corresponding pixel in the first and second digitalimages; comparing the pixels of the third and second digital images withone another; identifying pixels that changed between the second andthird digital images; and determining that an object identified in thesecond digital image has changed in the third digital image based on thecompared pixels of the second and third digital images that wereidentified to have changed. An object identified in the second digitalimage that has been determined to change in the third digital image maybe determined to be a seed object from which the extent of thedetermined change is evaluated. In such a case, the method performed bythe executed instructions may further comprise updating the coordinatesof the pixels associated with the object in the memory based on thepixels identified to have changed between the second and third digitalimages.

Another aspect of the present disclosure is directed to a method foridentifying growth in a culture media inoculated with a biologicalsample and disposed in a substantially transparent container. The systemcomprises: an image acquisition device for capturing digital images ofthe culture media; memory storing information regarding candidate colonyobjects identified in the captured digital images; and one or moreprocessors operable to execute instructions to perform a method. Themethod comprises: at the onset of incubation of the media (t₀),obtaining a first digital image of the media, the first digital imagehaving a plurality of pixels; assigning coordinates to one or morepixels of the first digital image; after a period of incubation for themedia (t_(x)), obtaining a second digital image of the media, the seconddigital image having a plurality of pixels; aligning the second digitalimage with the first digital image, wherein said alignment is based onthe coordinates assigned to the pixels of the first image and one ormore pixels of the second image corresponding to the pixels of the firstimage that were assigned the coordinates; generating spatial contrastdata indicative of changes between locally adjacent pixels of the seconddigital image; generating temporal contrast data indicative of changesbetween corresponding pixels of the first and second digital images; foreach of a plurality of pixels of the second digital image, assigning acontrast value to the pixel based on a combination of the spatialcontrast data and the temporal contrast data of the pixel; associatingadjacent pixels having contrast values that are greater than apredetermined threshold and within a predetermined margin of error ofone another, the associated pixels constituting an identified object;and storing each identified object in the memory as a candidate colony.

In some examples, combining the spatial and temporal contrast datacomprises averaging the spatial and temporal contrast data. The spatialand temporal contrast data may be combined according to a weightedaverage.

Generating spatial contrast data may comprise: obtaining a plurality ofimages at time t_(x), each of the plurality of images being obtainedunder different illumination conditions; processing spatial data in eachof the plurality of t₀ images; and combining the processed spatial data.Processing the spatial data may comprise separately processing spatialdata results for each illumination condition, and selecting a maximumresult from the separately processed spatial data results.

Generating temporal contrast data may comprise: obtaining a plurality ofimages at time t₀, each of the plurality of images being obtained underdifferent illumination conditions; obtaining a plurality of images attime t_(x), the illumination conditions of each image at time t_(x)corresponding to the illumination conditions of an image obtained attime t₀; processing temporal data in each of the corresponding t₀ andt_(x) images; and combining the processed temporal data. Processing thetemporal data may comprise separately processing temporal data resultsfor each illumination condition, and selecting a maximum result from theseparately processed temporal data results.

In some examples, the method may further comprise, for a givenidentified object: obtaining a plurality of object features from thepixel information associated with the object, wherein the objectfeatures comprise at least one morphometric feature, the morphometricfeature being at least one of an object shape, object area, objectperimeter or object edge; combining the object features using aclassification algorithm; comparing the combined object features toobject feature information for a plurality of microorganisms stored inthe memory; and classifying the identified object as a type ofmicroorganism based on the comparison. The object features may furthercomprise at least one spectral feature, the spectral feature being atleast one of an object color, object brightness, object hue, objectchroma, or at least one temporal feature (the temporal feature being atleast one of an object growth rate, a change in object color, or aprojected time that the object was first visually observable). At leastone of the object features may be obtained for each pixel of theidentified object, and then combined using one or more statisticalhistogram features. The classification algorithm may be a supervisedmachine learning algorithm, wherein the combined object features may becompared to object feature information for four or fewer microorganismsstored in the memory.

In some examples, the method may further comprise, for a givenidentified object: for each pixel of the identified object, assigning atemporal contrast value to the pixel based on the temporal contrast dataof the pixel; identifying one or more maxima from the assigned temporalcontrast values; if more than one maximum is identified, determiningwhether the maxima are associated with a common colony forming unit orwith different colony forming units; and for any two maxima determinedto be associated with different colony forming units, segmenting theidentified object into two objects based at least in part on therespective locations of said two maxima. Determining whether two maximaare associated with a common colony forming unit or with differentcolony forming units may further comprise: for each maximum, determininga distance from the maximum to an edge of the identified object;calculating an inclusion factor value based on each determined distanceand the distance between the two maxima; and comparing the inclusionfactor value to a predetermined range, wherein the maxima are associatedwith a common colony forming unit if the inclusion factor value is lessthan the predetermined range, and the maxima are associated withdifferent colony forming units if the inclusion factor value is greaterthan the predetermined range. If the inclusion factor value is withinthe predetermined range, the method may yet further comprise: for eachmaximum, determining a region surrounding the maximum; and calculating aconvexity of the respective regions surrounding the maxima, wherein ifthe convexity is greater than a threshold value, the maxima areassociated with different colony forming units.

In some examples, the method may further comprise: identifying one ormore objects in a digital image first in time based on the spatialcontrast data; and, for a given identified object in a digital imagesecond in time, if the combined spatial and temporal contrast data forthe object in the digital image second in time matches the spatialcontrast data for an object identified in the digital image first intime, classifying the object in the second digital image as an artifact.

Yet another aspect of the present disclosure is directed to a method forevaluating microbial growth on plated media that has been inoculatedwith a culture and incubated, the method comprising: obtaining first andsecond digital images of the plated media, each digital image obtainedafter a period of incubation for the inoculated media and at a differenttime; aligning the second digital image with the first digital image,wherein said alignment is based on the coordinates assigned to thepixels of the first image and one or more pixels of the second imagecorresponding to the pixels of the first image that were assigned thecoordinates; generating temporal contrast data indicative of changesbetween corresponding pixels of the first and second digital images;identifying an object in the second digital image from the temporalcontrast data; obtaining one or more dynamic object features of theidentified object from the temporal contrast data; classifying theidentified object as a type of organism based on the one or more dynamicobject features; and storing the identified object and itsclassification in the memory. In some examples, the one or more dynamicobject features may include a growth rate of the identified object, achange to a chromatic feature of the identified object, or a change ingrowth along an axis proximately normal to the plated media.

In some examples, the method may further comprise: obtaining a thirddigital image of the plated media after a period of incubation for theinoculated media and at a different time than the first and seconddigital images; aligning the third digital image with the first andsecond digital images; and generating temporal contrast data indicativeof changes between corresponding pixels of the second and third digitalimages, wherein the object is identified based further on said temporalcontrast data, and wherein the one or more dynamic object features ofthe identified object includes a second derivative of the temporalcontrast data. The one or more dynamic object features may includeincludes an object growth acceleration rate.

Yet a further aspect of the present disclosure is directed tocomputer-readable memory storage medium having program instructionsencoded thereon configured to cause a processor to perform a method. Themethod may be any of the above methods for evaluating microbial growthon plated media, identifying microbial growth on plated media that hasbeen inoculated with a culture and incubated, or evaluating microbialgrowth on plated media that has been inoculated with a culture andincubated.

An even further aspect of the present is directed to a system forevaluating growth in a culture media inoculated with a biologicalsample. The system comprises an image acquisition device for capturingdigital images of the culture media, memory, and one or more processorsoperable to execute instructions to perform a method. In some examples,the memory may store information regarding the captured digital images,and the method performed by the executed instructions may be any one theabove described methods for evaluating microbial growth on plated media.In other examples, the memory may store information regarding candidatecolony objects identified in the captured digital images, and the methodperformed by the executed instructions may be any one the abovedescribed methods for identifying microbial growth on plated media thathas been inoculated with a culture and incubated. In yet furtherexamples, the memory may store information regarding one or more objectsidentified in the captured digital images, and one or moreclassifications of the identified objects, and the method performed bythe executed instructions may be any one the above described methods forevaluating microbial growth on plated media that has been inoculatedwith a culture and incubated.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for imaging analyzing andtesting a culture according to an aspect of the disclosure.

FIG. 2 is a flow chart illustrating an automated laboratory workflowroutine for imaging analyzing and testing a culture according to anaspect of the disclosure.

FIGS. 3A, 3B, and 3C are images showing a temporal contrast by a visualrepresentation of colony morphology as it changes over time according toan aspect of the disclosure.

FIGS. 3D and 3E are images showing spatial contrast under differentillumination conditions.

FIG. 4 is a flow chart of an example routine for obtaining and analyzingimage information according to an aspect of the disclosure.

FIG. 5 is a flow chart of an example routine for obtaining spatialcontrast according to an aspect of the disclosure.

FIG. 6 is a flow chart of an example routine for obtaining temporalcontrast according to an aspect of the disclosure.

FIG. 7 is a flow chart of an example routine for filtering artifactsfrom an image according to an aspect of the disclosure.

FIG. 8 is a flow chart of an example routine for labeling pixels of animage according to an aspect of the disclosure.

FIG. 9 is a flow chart of an example routine for separating coloniesinto separate objects according to an aspect of the disclosure.

FIG. 10 is a flow chart of an example object segmentation routineaccording to an aspect of the disclosure.

FIG. 11 is an illustration showing measurements of confluent colonies aspart of the segmentation routine of FIG. 10.

FIG. 12 is a Voronoï diagram according to an aspect of the disclosure.

FIGS. 13A, 13B and 13C are diagrams illustrating isolation factormeasurements according to an aspect of the disclosure.

FIG. 14 is a diagram illustrating Voronoï regions of influence accordingto an aspect of the disclosure.

FIGS. 15A, 15B and 15C are images illustrating a characterization ofcolony growth according to an aspect of the disclosure.

FIGS. 16A and 16B show a section of an imaged plate, with zoomed andreoriented images of sample colonies of the image.

FIG. 16C shows vector diagrams of the respective images of FIG. 16B.

FIG. 17 depicts SHQI, spatial contrast, and temporal contrast images ofa specimen according to an aspect of the disclosure.

FIG. 18 is a flow chart comparing the timeline of the routine of FIG. 2to the timeline of a comparable manually-performed routine.

DETAILED DESCRIPTION

The present disclosure provides apparatus, systems and methods foridentifying and analyzing microbial growth in on plated media based inat least in part on contrast detected in one or more digital images ofthe plated media. Many of the methods described herein can be fully orpartially automated, such as being integrated as part of a fully orpartially automated laboratory workflow.

The systems described herein are capable of being implemented in opticalsystems for imaging microbiology samples for the identification ofmicrobes and the detection of microbial growth of such microbes. Thereare many such commercially available systems, which are not described indetail herein. One example is the BD Kiestra™ ReadA Compact intelligentincubation and imaging system. Other example systems include thosedescribed in PCT Publication No. WO2015/114121 and U.S. PatentPublication 2015/0299639, the entirety of which is incorporated byreference herein. Such optical imaging platforms are well known to thoseskilled in the art and not described in detail herein.

FIG. 1 is a schematic of a system 100 having a processing module 110 andimage acquisition device 120 (e.g., camera) for providing high qualityimaging of plated media. The processing module and image acquisitiondevice may be further connected to, and thereby further interact with,other system components, such as an incubation module (not shown) forincubating the plated media to allow growth of a culture inoculated onthe plated media. Such connection may be fully or partially automatedusing a track system that receives specimens for incubation andtransports them to the incubator, and then between the incubator andimage acquisition device.

The processing module 110 may instruct the other components of thesystem 100 to perform tasks based on the processing of various types ofinformation. The processor 110 may be hardware that performs one or moreoperations. The processor 110 may be any standard processor, such as acentral processing unit (CPU), or may be a dedicated processor, such asan application-specific integrated circuit (ASIC) or a fieldprogrammable gate array (FPGA). While one processor block is shown, thesystem 100 may also include multiple processors which may or may notoperate in parallel, or other dedicated logic and memory for storing andtracking information related to the sample containers in the incubatorand/or image acquisition device 120. In this regard, the processing unitmay track and/or store several types of information regarding a specimenin the system 100, including but not limited to the location of thespecimen in the system (incubator or image acquisition device, locationsand/or orientation therein, etc.), the incubation time, pixelinformation of captured images, the type of sample, the type of culturemedia, precautionary handling information (e.g., hazardous specimens),etc. In this regard, the processor may be capable of fully or partiallyautomating the various routines described herein. In one embodiment,instructions for performing the routines described herein may be storedon a non-transitory computer-readable medium (e.g. a software program).

FIG. 2 is a flow chart showing an example automated laboratory routine200 for imaging, analyzing and, optionally, testing a culture. Theroutine 200 may be implemented by an automated microbiology laboratorysystem, such as the BD Kiestra™ Total Lab Automation or BD Kiestra™ WorkCell Automation. The example systems include interconnected modules,each module configured to execute one or more steps of the routine 200.

At 202, a culture medium is provided and inoculated with a biologicalsample. The culture medium may be an optically transparent container,such that the biological sample may be observed in the container whileilluminated from various angles. Inoculation may follow a predeterminedpattern. Streaking patterns and automated methods for streaking a sampleonto a plate are well known to one skilled in the art and not discussedin detail herein. One automated method uses magnetically controlledbeads to streak sample onto the plate. At 204, the medium is incubatedto allow for growth of the biological sample.

At 206, one or more digital images of the medium and biological sampleare captured. As will be described in greater detail below, digitalimaging of the medium may be performed multiple times during theincubation process (e.g., at the start of incubation, at a time in themiddle of incubation, at the end of incubation) so that changes in themedium may be observed and analyzed. Imaging of the medium may involveremoving the medium from the incubator. Where multiple images are takenof the medium at different times, the medium may be returned to theincubator for further incubation between imaging sessions.

At 208, the biological sample is analyzed based on information from thecaptured digital images. Analysis of the digital image may involveanalysis of pixel information contained in the image. In some instances,pixel information may be analyzed on a pixel by pixel basis. In otherinstances, pixel information may be analyzed on a block by block basis.In yet further instances, pixels may be analyzed based on entire regionsof pixels, whereby the pixel information of individual pixels in theregion may be derived by combining information of the individual pixels,selecting sample pixels, or by using other statistical methods such asthe statistical histogram operations described in greater detail below.In the present disclosure, operations that are described as beingapplied to “pixels” are similarly applicable to blocks or othergroupings of pixels, and the term “pixel” is hereby intended to includesuch applications.

The analysis may involve determining whether growth is detected in themedium. From an image analysis perspective, growth can be detected in animage by identifying an imaged object (based on differences between theobject and its adjacent surroundings) and then identifying changes inthe object over time. As described in greater detail herein, thesedifferences and changes are both forms of “contrast.” In addition todetecting growth, the image analysis at 108 may further involvequantifying the amount of growth detected, identifying distinctcolonies, identifying sister colonies, etc.

At 210, it is determined whether the biological sample (particularly,the identified sister colonies) exhibits quantitatively significantgrowth. If no growth, or an insignificant amount of growth, is found,then the routine 200 may proceed to 220, in which a final report isoutput. In the case of proceeding from 210 to 220, the final report willlikely indicate the lack of significant growth, or report the growth ofnormal flora.

If it is determined that the biological sample exhibits quantitativelysignificant growth, then at 212, one or more colonies may be picked fromthe images based on the prior analysis. Picking colonies may be a fullyautomated process, in which each of the picked colonies is sampled andtested. Alternatively, picking colonies may be a partially automatedprocess, in which multiple candidate colonies are automaticallyidentified and visually presented in a digital image to an operator,such that the operator may input a selection of one or more candidatesfor sampling and further testing. The sampling of selected or pickedcolonies may itself be automated by the system.

At 214, a sampled colony is prepared for the further testing, such as byplating the sample in an organism suspension. At 216, the sample istested using matrix-assisted laser desorption ionization (MALDI) imagingto identify the type of specimen that was sampled from the originalmedium. At 218, the sample is also, or alternatively, subjected toantibiotic susceptibility testing (AST) to identify possible treatmentsfor the identified specimen.

At 220, the testing results are output in a final report. The report mayinclude the MALDI and AST results. As mentioned above, the report mayalso indicate a quantification of specimen growth. Thus, the automatedsystem is capable of beginning with an inoculated culture medium andgenerating a final report regarding a specimen found in the culture,with little or no additional input.

In routines such as the example routine of FIG. 2, the detected andidentified colonies are often referred to as Colony Forming Units(CFUs). CFUs are microscopic objects that begin as one or a fewbacteria. Over time, the bacteria grow to form a colony. The earlier intime from when the bacteria are placed in the plate, the less bacteriathere is to detect and, consequently the smaller the colony and thelower that contrast to the background. Stated another way, a smallercolony size yields a smaller signal, and a smaller signal on a constantbackground results in smaller contrast. This is reflected by thefollowing equation:

$\begin{matrix}{{Contrast}{= \frac{{Signal} - {background}}{{Signal} + {background}}}} & (1)\end{matrix}$

Contrast can play an important role in identifying objects, such as CFUsor other artifacts, in the images. An object can be detected in an imageif it is significantly different in brightness, color and/or texturefrom its surroundings. Once an object has been detected, the analysismay also involve identifying the type of object that has been detected.Such identifications can also rely on contrast measurements, such as thesmoothness of edges of the identified object, or the uniformity (or lackof uniformity) of the color and/or brightness of the object. Thiscontrast must be great enough to overcome the image noise (backgroundsignals) in order to be detected by the image sensor.

The human perception of contrast (governed by Weber's law) is limited.Under optimal conditions, human eyes can detect a light level differenceof 1%. The quality and confidence of image measurements (e.g.,brightness, color, contrast) may be characterized by a signal-to-noiseratio (SNR) of the measurements, in which an SNR value of 100 (or 40db), independent from pixel intensities, would match human detectioncapabilities. Digital imaging techniques utilizing high SNR imaginginformation and known SNR per pixel information can allow for detectionof colonies even when those colonies are not yet visible to human eyes.

In the present disclosure, contrast may be collected in at least twoways: spatially and temporally. Spatial contrast, or local contrast,quantifies the difference in color or brightness between a given region(e.g., pixel, group of adjacent pixels) and its surroundings in a singleimage. Temporal contrast, or time contrast, quantifies the difference incolor or brightness between a given region of one image against thatsame region in another image taken at a different time. The formulagoverning temporal contrast is similar to that for spatial contrast:

$\begin{matrix}{{{Temporal}\mspace{14mu}{Contrast}} = \frac{{{{Signal}( t_{0} )} - {{Signal}( t_{1} )}}}{{{Signal}( t_{0} )} + {{Signal}( t_{1} )}}} & (2)\end{matrix}$

In which t₁ is a time subsequent to t₀. Both spatial and temporalcontrasts of a given image may be used to identify objects. Theidentified objects may then be further tested to determine theirsignificance (e.g., whether they are CFUs, normal flora, dust, etc.).

FIGS. 3A, 3B and 3C provide a visual demonstration of the effect thattemporal contrast can have on an imaged sample. The images shown in FIG.3A were captured at different points in time (left to right, top row tobottom row) showing the overall growth in the sample. While growth innoticeable in FIG. 3A, the growth is even more noticeable, and can benoticed even earlier in the sequence, from the corresponding contrasttemporal images of FIG. 3B. For purposes of clarity, FIG. 3C shows azoomed section of FIG. 3B. As can be seen in FIG. 3C, the longer aportion of a colony has been imaged, the brighter a spot it makes in thecontrast image. In this way, the center of mass of each colony may bedenoted by the bright center, or peak, of the colony. Thus, image dataobtained over time can reveal important information about changes incolony morphology.

To maximize spatial or temporal contrast of an object against itsbackground, the system may capture images using different incidentlights on different backgrounds. For instance, any of top lighting,bottom lighting, or side lighting may be used on either a black or whitebackground.

FIGS. 3D and 3E provide a visual demonstration of the effect thatlighting conditions can have on an imaged sample. The image in FIG. 3Dwas captured using top lighting, whereas the image in FIG. 3E wascaptured at approximately the same time (e.g., close enough in time thatno noticeable or significant growth has occurred) using bottom lighting.As can be seen, each of the images in the samples of FIGS. 3D and 3Econtains several colonies, but additional information about the colonies(in this case, hemolysis) can be seen thanks to the back-lighting orbottom lighting in the image of FIG. 3D, whereas that same informationis difficult to grasp in the image of FIG. 3E.

At a given point in time, multiple images may be captured under multipleillumination conditions. Images may be captured using different lightsources that are spectrally different due to illumination light level,illumination angle, and/or filters deployed between the object and thesensor (e.g. red, green and blue filters). In this manner, the imageacquisition conditions may be varied in terms of light source position(e.g., top, side, bottom), background (e.g., black, white, any color,any intensity), and light spectrum (e.g. red channel, green channel,blue channel). For instance, a first image may be captured using topillumination and a black background, a second image captured using sideillumination and a black background, and a third image captured usingbottom illumination and no background (i.e. a white background).Furthermore, specific algorithms may be used to create a set of varyingimage acquisition conditions in order to maximize spatial contrastusing. These or other algorithms can also be useful to maximize temporalcontrast by varying the image acquisition conditions according to agiven sequence and/or over a span of time. Some such algorithms aredescribed in PCT Publication No. WO2015/114121.

FIG. 4 is a flow chart showing an example routine for analyzing animaged plate based at least in part on contrast. The routine of FIG. 4may be thought of as an example subroutine of the routine 200 of FIG. 2,such that 206 and 208 of FIG. 2 are carried out at least in part usingthe routine of FIG. 4.

At 402, a first digital image is captured at time t₀. Time t₀ may be atime shortly after the incubation process has begun, such that bacteriain the imaged plate have not yet begun to form visible colonies.

At 404, coordinates are assigned to one or more pixels of the firstdigital image. In some instances, the coordinates may be polarcoordinates, having a radial coordinate extending from a center point ofthe imaged plate and an angular coordinate around the center point. Thecoordinates may be used in later steps to help align the first digitalimage with other digital images of the plate taken from different anglesand/or at different times. In some cases, the imaged plate may have aspecific landmark (e.g., an off-center dot or line), such thatcoordinates of the pixel(s) covering the landmark in the first image maybe assigned to the pixel(s) covering the same landmark in the otherimages. In other cases, the image itself can be considered as a featurefor future alignment.

At 406, a second digital image is captured at time t_(x). Time t_(x) isa time after t₀ at which the bacteria in the imaged plate has had anopportunity to form visible colonies.

At 408, the second digital image is aligned with the first digital imagebased on the previously assigned coordinates. Aligning the images mayfurther involve normalization and standardization of the images, forinstance, using the methods and systems described in PCT Publication No.WO2015/114121.

At 410, contrast information of the second digital image is determined.The contrast information may be gathered on a pixel-by-pixel basis. Forexample, the pixels of the second digital image may be compared with thecorresponding pixels (at the same coordinates) of the first digitalimage to determine the presence of temporal contrast. Additionally,adjacent pixels of the second digital image may be compared with oneanother, or with other pixels known to be background pixels, todetermine the presence of spatial contrast. Changes in pixel colorand/or brightness are indicative of contrast, and the magnitude of suchchanges from one image to the next or from one pixel (or region ofpixels) to the next, may be measured, calculated, estimated, orotherwise determined. In cases where both temporal contrast and spatialcontrast are determined for a given image, an overall contrast of agiven pixel of the image may be determined based on a combination (e.g.,average, weighted average) of the spatial and temporal contrasts of thatgiven pixel.

At 412, objects in the second digital image are identified based on thecontrast information computed at 410. Adjacent pixels of the seconddigital image having similar contrast information may be considered tobelong to the same object. For instance, if the difference in brightnessbetween the adjacent pixels and their background, or between the pixelsand their brightness in the first digital image, is about the same(e.g., within a predetermined threshold amount), then the pixels may beconsidered to belong to the same object. As an example, the system couldassign a “1” to any pixel having significant contrast (e.g., over thethreshold amount), and then identify a group of adjacent pixels allassigned “1” as an object. The object may be given a specific label ormask, such that pixels with the same label share certaincharacteristics. The label can help to differentiate the object fromother objects and/or background during later processes of the subroutine400. Identifying objects in a digital image may involve segmenting orpartitioning the digital image into multiple regions (e.g., foregroundand background). The goal of segmentation is to change the image into arepresentation of multiple components so that it is easier to analyzethe components. Image segmentation is used to locate objects of interestin images.

At 414, the features of a given object (identified at 412) may becharacterized. Characterization of an object's features may involvederiving descriptive statistics of the object (e.g., area, reflectance,size, optical density, color, plate location, etc.). The descriptivestatistics may ultimately quantitatively describe certain features of acollection of information gathered about the object (e.g., from a SHQIimage, from a contrast image). Such information may be evaluated as afunction of species, concentrations, mixtures, time and media. However,in at least some cases, characterizing an object may begin with acollection of qualitative information regarding the object's features,whereby the qualitative information is subsequently representedquantitatively. Table 1 below provides a list of example features thatmay be qualitatively evaluated and subsequently converted to aquantitative representation:

TABLE 1 Qualitative Attributes of Objects, and Criteria forQuantitatively Converting the Attributes Number Feature Score Criteria 1Growth 0 No growth 1 Growth 2 Expected Time to Visually Observe n/aRecord time in hours 3 Size (diameter) 1 <1 mm 2 >1-4 mm  3 >4 mm 4Growth Rate (Δ diameter/2 hrs) 1 <1 mm 2 >1-2 mm  3 >2 mm 5 Color 1grey/white 2 rose-pink 3 colorless 4 red 5 blue 6 blue-green 7 brown 8pale yellow to yellow 9 green 6 Hemolysis 0 none 1 small beta (<1 mm) 2large beta (>1 mm) 3 alpha 7 Shape 1 convex 2 flat 3 spread 4 Concave 8Surface/Edge 1 smooth 2 rough 3 mucoid 4 feet

Some features of an object, such as shape or the time until it isobserved visually, may be measured a single time for the object as awhole. Other features may be measured several times (e.g., for eachpixel, for every row of pixels having a common y-coordinate, for everycolumn of pixels having a common x-coordinate, for every ray of pixelshaving a common angular coordinate, for a circle of pixels having acommon radial coordinate) and then combined, for instance using ahistogram, into a single measurement. For example, color may be measuredfor each pixel, growth rate or size for every row, column, ray or circleof pixels, and so on.

At 416, it is determined whether the object is a colony candidate basedon the characterized features. The colony candidate determination mayinvolve inputting the quantitative features (e.g., the scores shown inTable 1, above), or a subset thereof, into a classifier. The classifiermay include a confusion matrix for implementing a supervised machinelearning algorithm, or a matching matrix for implementing anunsupervised machine learning algorithm, to evaluate the object.Supervised learning may be preferred in cases where an object is to bediscriminated from a limited set (e.g., two or three) of possibleorganisms (in which case the algorithm could be trained on a relativelylimited set of training data). By contrast, unsupervised learning may bepreferred in cases where an object is to be discriminated from an entiredatabase of possible organisms, in which case it would be difficult toprovide comprehensive—or even sufficient—training data. In the case ofeither confusion or a matching matrix, differentiation could be measurednumerically on a range. For instance, for a given pair of objects, a “0”could mean the two objects should be discriminated from each other,whereas a “1” could mean that the objects are difficult to differentiateone from the other.

Colony candidates may be stored in a memory of the automated system forfurther use (e.g., testing, the segmentation routine described below,etc.).

Use of Multiple Media

In the above examples, evaluation of a culture is described for a singlemedia. However, the examples are similarly applicable to instances wherea culture is evaluated in multiple media.

Since the characteristics of bacteria (e.g., color, growth rate, etc.)may vary depending on the type of culture media (“media”) used,different confusion matrices may be applied for each medium during theclassification (e.g., 416 of subroutine 400). Thus, it is fully withinreason that the classifier for one media would output a “0” for twoobjects, whereas a classifier for a different media would output a “1”for the same two objects. The collective results of the classifierscould then be evaluated together (manually or based on furthermachine-driven relationships) to arrive at an overall or finaldifferentiation or classification for the objects.

Evaluation of multiple media may be implemented using a singlecontainer. The single container may be configured to hold multiple media(e.g., bi-plate, tri-plate quadplate, etc.) such that the multiple mediamay be imaged together at the same time. Alternatively, multiple mediamay be evaluated by streaking a culture sample in several containers,each container holding one or more media. Each of the multiplecontainers may then be subjected to the imaging routines describedabove. The information derived from each of the media (e.g.,characterized features) may then be collectively inputted into theclassifier in order to make an even more informed identification of thegrowth spotted in the various media.

Contrast Information

FIG. 5 is a flow diagram showing an example subroutine 500 for obtainingspatial contrast as part of 410 of FIG. 4. The subroutine 500 receivesas inputs: a set of one or more background and lighting conditions 551and a filter 554. At 502, the digital image is obtained under aspecified lighting and background condition from the input set 551. At504, the image is then cloned. At 506, one of the cloned images isfiltered using the filter 554. In the example of FIG. 5 a low passkernel is used as the filter, but the skilled person is aware of otherfilters that might be used. At 508, a ratio of the filtered imagesubtracted from the unfiltered image, and the filtered image added tothe unfiltered image, is computed. At 510, a spatial contrast image isobtained based on the computed ratio of 508. This routine 500 may berepeated for each of the background and lighting conditions 551. Eachrepetition of the routine 500 results in another spatial contrast image,which may be used to iteratively update the previously stored spatialcontrast image at 510. Thus, a comprehensive contrast image (includingcontrast from each of the illumination conditions) may be iterativelybuilt. In one embodiment, in each iteration, the cleared contrast image,in which the contrast settings are still set to zero (as compared to theiteratively built contrast image) may be provided as an input for eachillumination setting. If it is determined at 512 that the last image hasbeen processed, then routine 500 ends.

FIG. 6 is a flow diagram showing an example subroutine 600 for obtainingtemporal contrast also as part of 410 of FIG. 4. The subroutine 600receives as inputs: a set of one or more background and lightingconditions 651; and a filter 655. At 602, each of the first and seconddigital images taken under specific lighting and background conditionsis obtained. At 604, the to image is filtered. In the example of FIG. 6a low pass kernel is used as the filter, but the skilled person is awareof other filters that might be used. At 606, a ratio of the filtered t₀image subtracted from the unfiltered t_(x) image, and the filtered t₀image added to the unfiltered t_(x) image, is computed. At 608, atemporal contrast image is obtained based on the computed ratio of 606.This routine 600 may be repeated under different illumination conditionsand/or different background conditions. Each repetition of the routine600 results in another temporal contrast image, which may be used toiteratively update the previously stored temporal contrast image at 608.As with the building of a spatial contrast image, a temporal contrastimage may be built iteratively, with a cleared contrast image providedan input for each illumination condition. If it is determined at 610that the last image has been processed, then routine 600 ends.

Spatial and temporal contrast results may further be combined in orderto make a comprehensive or overall determination regarding contrast. Thecombination of spatial and temporal contrast is herein referred to as“mixed contrast” (MC). In one embodiment, mixed contrast may be derivedfrom a spatial contrast (SC) image at time t₀, a spatial contrast imageat time t_(x), and a temporal contrast (TC) image derived from acomparison of t₀ and t_(x) images, according to the following equation:

$\begin{matrix}{{MC^{({t_{0},t_{x}})}} = \frac{{TC}^{({t_{0},t_{x}})} + ( {{SC^{t_{x}}} - {SC^{t_{0}}}} )}{2}} & (3)\end{matrix}$

Filtering

Additional processes may be included in the subroutine 400 of FIG. 4 inorder to enhance the image analysis. For example, the first digitalimage may be analyzed for objects that appear in the image at time t₀.Since it is known that no bacteria have yet begun to significantly growat t₀, it can be assumed that any objects spotted at time t₀ are merelydust, air bubbles, artifacts, condensation, etc. that would notconstitute a colony candidate.

One filtering process could be used on a captured image to subtract dustand other artifacts that land on the imaged plate or lens. Whenconsidering transparent media (e.g., MacConkey's agar, CLED agar,CHROMagar, etc.), some level of dust is expected to be present on acaptured image. The impact of the dust on a given image may be dictatedat least in part based on the particular lighting and backgroundconditions under which the image is taken. For example, when using whitemedia, reflective artifacts and dust will be most observable when themedia is illuminated from above with black background underneath. Asanother further example, when using colored or dark media, artifacts anddust will be most observable when the media is illuminated from abovewith a white background underneath. As a further example, in most anymedia, artifacts and dust that absorb light will be observable when themedia is illuminated from underneath, regardless of background. In anycase, management of dust and artifacts is a complex image processingchallenge that can significantly impact detection of microbial growth.

Dust and artifacts can be broken down into two types: (A) those that arecapable of changing position; and (B) those that are not capable ofchanging position. Dust and artifacts can accumulate over time, meaningthe number of both types A and B may vary over time. Nonetheless,observations have shown that type A is more prone to change in quantityover time than is type B. Of course, type A is also more prone tochange, such as due to the plate being moved into or out of the imagingchamber.

Generally, type B is caused by artifacts that are linked to the plateitself, such as ink dots (brand, lot number and information printedunderneath the plate), imperfections linked to the plastic moldinjection point, or a frosted region. Type B can also be caused by dustor air bubbles stuck on top of the media, trapped inside the media, orelectrostatically stuck to the underside of the plate.

From an imaging point of view, even type A dust and artifacts are bythemselves mostly unchanging in position. However, due to the plastic ofthe plate and the media acting as a filter and lens, the observedcharacteristics and position of the type A artifacts may change slightlydepending upon the media color, the media level, and the plastic. Type Bdust and artifacts are also unchanging in position. However, to theextent that type B dust and artifacts are connected to the media, andthe media is subject to slighting movement and shifting over time(mostly due to slight desiccation over time in the incubator), the typeB dust and artifacts can move with the media. Therefore, the position oftype B dust and artifacts is also at least somewhat prone to subtlechanges.

In terms of contrast, a speck of type A dust can be said to be presentin the t₀ spatial contrast image at a position “p₀,” and in the t_(x)spatial contrast image at a position “p_(x).” Assuming p₀ and p_(x) aredifferent locations, then the dust or artifact will also be present in atemporal contrast image at both locations (e.g., showing positivecontrast in the p_(x) location, and negative contrast in the p₀location). By comparison, a speck of type B dust will be present in acommon location of both spatial contrast images at times t₀ and t_(x),yet absent from the temporal contrast image.

As explained above, spatial and temporal contrast images can be combinedin order to derive mixed contrast results. The impact of both types Aand B dust and artifacts can further be eliminated from the mixedcontrast results. In one embodiment, if an object (e.g., a CFUcandidate) is identified in the mixed contrast result, it may becompared to the dust and artifacts detected with the neighborhood N(x,y)of the object in the spatial contrast result at time t₀. Then, if asimilar object is found in the spatial contrast result at time t₀, theobject identified in the mixed contrast result is flagged as an A typeor B type false positive. Even if an object is not flagged as an A typeor B type false positive at first, if over time the object is found tonot significantly change size, it may still later be determined that theobject is a B type false positive. The false positives may be stored,and later applied to subsequent images, such as through the filteringmasks (e.g., binary mask) described further below.

Another filtering process could be used to subtract condensation formedon the plate (e.g., during transit from fridge to incubator at thebeginning of an incubation session). In one example condensation filter,the plate is illuminated using bottom lighting, so that less lightpenetrates through locations of condensation than locations withoutcondensation. The optical density of the image may then be evaluated,and areas of low optical density could be subtracted from the image.

Additionally or alternatively, an image mask could be constructed todiscount objects from any analysis of the t₀ image and/or subsequentdigital images. FIG. 7 is a flow diagram showing an example routine 700for creating an image mask using spatial contrast of the t₀ image. Inthe example of routine 700, the only input provided is the SHQI image751 taken at time t₀. At 702, spatial contrast of the t₀ image isdetermined. At 704, the spatial contrast information is used to collectstatistical information regarding the pixels in the region of interestof the t₀ image, such as mean and standard deviation (e.g., ofbrightness). At 706, a contrast threshold is adjusted to ensure that anappropriate number of pixels exceed that threshold. For example, if morethan a given percentage of pixels are not deemed background of theimage, then the threshold may be increased. At 708, the threshold isfurther adjusted based on statistical information regarding those pixelsunder the threshold. Finally, at 710, a binary mask is generated. Thebinary mask differentiates between various artifacts that are not validpixels, and other pixels which are considered valid. The binary maskcould then be used at a subsequent time when there are potentialcolonies to detect, and to rule out objects occupying the not-validpixels from being candidate colonies.

The above filtering processes could improve subroutine 400, by avoidingaccidental inclusion of dust, condensation, or other artifacts asobjects, and speeding up the property characterization at 414 since suchcharacterization would only have to be performed for valid pixels.

Defining Objects and Labels

Another process that may be added to subroutine 400 of FIG. 4 isassigning labels to the objects identified at 412. The object may begiven a specific label, such that pixels with the same label sharecertain characteristics. The label can help to differentiate the objectfrom other objects and/or background during later processes of thesubroutine 400. FIG. 8 is a flow diagram showing an example routine 800for labeling the pixels of an image taken at time t_(x) (or “t_(x)image”). In the example of FIG. 8, a binary mask 851 (e.g., the outputof routine 700), an uninitialized candidate mask 852 for the t_(x)image, and a temporal contrast image 853 (e.g., the output of subroutine600) are received as inputs. At 802, the candidate mask 852 isinitialized. Initialization may involve identifying a region of intereston the imaged plate, as well as using the binary mask 851 to identify“valid pixels” in the image taken at time t_(x). Valid pixels are pixelsof the imaged that have not been discounted as candidate colonies, andwill be considered for labeling. At 804, the temporal contrast image 853is used to collect statistical information regarding the valid pixels inthe region of interest of the t_(x) image, such as mean and standarddeviation (e.g., of brightness). Then, at 806, the statisticalinformation of each of the temporal contrast image 853 and the binarymask 851 (which are preferably generated under similar lighting andbackground conditions) are combined to form a threshold contrast image.Using the threshold defined by the threshold contrast image, “connexcomponents” of the t_(x) image are labeled at 808. A connex component iseffectively a label indicating a connection between (or grouping among)adjacent pixels, which in turn indicates that the pixels are part of thesame object.

Once the connex components have been defined for the tx image, eachconnex component may be individually analyzed (at 810) to validate itsstatus as a single object. In the example of FIG. 8, a statisticalcomputation of the pixels associated with the label is made at 812. Thecomputation may utilize a histogram to determine mean and/or standarddeviation of brightness or color of the pixels. At 814, it is determinedwhether the pixels meet a threshold area. If the threshold area is notmet, then operations proceed to 830, in which the label is updated.Updating a label may involve either keeping the analyzed component asone label, or dividing the component up into two labels. In the case ofthe threshold area not being met, the component is kept as a singlelabel. If the threshold area is met, then at 816, the histogram issmoothed, and at 818, peaks of the distributed labeled pixels areidentified. Peaks may be further defined by having a minimum area, sincethat peaks smaller than the minimum area may be disregarded. At 820, thenumber of identified peaks is counted. If there is only one peak, thenoperations proceed to 830, and the label is updated, whereby thecomponent is kept as one object. If there is more than one peak, then at824, the threshold contrast image is used to further assess whether thecontrast between the peaks is significant. Operations then proceed to830, and the label is updated based on the multiple identified peaks,whereby significant contrast results in the component being divided intotwo, and otherwise being kept as one.

Segmentation

Another process that may be included as part of subroutine 400 is asegmentation process for separating confluent colonies at time t_(x)into separate objects. If at time t_(x) the colonies have grown to thepoint where they overlap or touch one another, it may be required todraw a boundary through the confluent region in order to evaluateseparate colonies in the region.

In some instances, where two bordering colonies have different features(e.g., different color, different texture), segmentation may simplyinvolve feature analysis of the confluent region. However, spatial andtemporal contrast alone are not always enough to identify a boundarybetween the colonies. FIG. 9 is a flow diagram showing an exampleroutine 900 for separating such colonies into separate objects (e.g.,with separate labels), or in other terms, segmenting the colonies. Theexample routine 900 of FIG. 9 uses a first digital image 951 taken attime t₀, a second digital image taken at time t_(x) and a t₀ imagebinary mask 953 (e.g., the mask generated by routine 700) as inputs. At902, a temporal contrast image is generated based on the t₀ and t_(x)images 951 and 952. At 904, the temporal contrast image is segmentedusing the binary mask 953. At 906, labels are applied to the segments ofthe image. At 908, peaks or maxima of each label are identified. Themaximum of a given segment is generally the centerpoint or center ofmass of the segment. At 910, for each label, the maxima (e.g., of thelabel under analysis, of neighboring labels) are used make furtherdeterminations as to whether a given label is unique to its neighbors,or should be combined with one or more neighboring labels. Once thelabels have been pared down to their unique components, characterizationof the features for each label (e.g., steps 414 and 416 of routine 400)may be performed at 912, and a global list of candidate colonies may begenerated at 914.

Various factors, such as inclusion factors, may be applied to determineif local maxima of a given label belong to one colony or to differentcolonies. Inclusion factors are factors that indicate whether or notneighboring pixels are associated with an adjacent object. Such factorsmay be used in a segmentation strategy to determine whether to split twolocal maxima in a given label into two separate objects, or merge theminto a single object.

FIG. 10 is a flow diagram showing such an example segmentation strategy.The routine 1000 may be used as a subroutine of step 910 in FIG. 9. Asshown in FIG. 10, two local maxima 1051 and 1052 are identified. At1002, a surrounding region is identified for each maximum. In theexample of FIG. 10, region “A” surrounds maximum 1051, and region “B”surrounds maximum 1052. For purposes of the example equations below, itis assumed that region A is larger or equal in size to region B. In someinstances, each region may be given an oval shape having a horizontaldistance (xA, xB) along a horizontal axis of the region and a verticaldistance (yA, yB) along a vertical axis of the region. FIG. 11 providesan example illustration of regions A and B and their respective maximain order to clarify the routine of FIG. 10.

At 1004, a distance from the maximum to an edge of the object isdetermined for each local maximum 1051 and 1052. In some instances, thedetermined distance is an either an average or median distance of theregion assigned at 1002, hereinafter referred to as a distance map. Thedistance map of region A is hereinafter referred to as rA, and that ofregion B as rB.

At 1006, an inclusion factor is calculated based on a distance “d”between the two local maxima and the distances determined at 1004. Inone embodiment, the inclusion factor is calculated using the followingequation:

$\begin{matrix}{{Inclusion}\mspace{14mu}{Factor}{= \frac{d - {rA} + {rB}}{2{rB}}}} & (4)\end{matrix}$

At 1008, it is determined whether the inclusion factor is less than,greater than, or within a predetermined range (e.g., between 0.5 and 1).If the inclusion factor is less than the predetermined range, the maximaare determined to be associated with the same object. If it is greaterthan the predetermined range, the maxima are determined to be associatedwith separate objects.

For inclusion factors falling within the range, it is not immediatelyclear whether the maxima belong to the same or different objects, andmore processing is needed. The routine 1000 then continues at 1010, inwhich the convexity of the respective surrounding regions of the twomaxima is calculated using the coordinates of a third region “C” at aposition between the two maxima. In some instances, the region may be aweighted center of the two regions, such that the center point of regionC is closer to the smaller region B than to the larger region A.Horizontal and vertical distances xC and yC, and a distance map H, mayalso be calculated for region C. For example, the convexity may becalculated using the above values and d(A,C), which is the distancebetween the center points of region C and maximum A, according to thefollowing equations:

$\begin{matrix}{{xC} = \frac{{xA} + {( {{xB} - {xA}} )*{distOffset}}}{d}} & (5) \\{{yC} = \frac{{yA} + {( {{yB} - {yA}} )*{distOffset}}}{d}} & (6) \\{R = {{rA} + {( {{rB} - {rA}} )*\frac{d( {A,C} )}{d}}}} & (7) \\{{\Delta\; H} = {H - ( {{0.9}R} )}} & (8)\end{matrix}$

At 1012, it is determined whether the convexity value is greater (moreconvex) than a given threshold. For example, ΔH may be compared to athreshold value of 0. If convexity value is greater than the thresholdvalue, the maxima are determined to be associated with separate objects.Otherwise, at 1014, one or more parameters of region C are updated suchthat the size of region C is increased. For example, distOffset may beupdated based on ΔH, e.g., ΔH is capped at a value between 0 and 1 (ifΔH is greater than 1, it is rounded to 1) and is then added todistOffset.

At 1016, it is determined whether the size of region C meets or exceedsa threshold value. If this threshold value is met or exceeded, then themaxima are determined to be associated with the same object. In otherwords, if the difference between regions A and B is so indeterminationthat region C is increased until it begins to overshadow regions A andB, this is a good indication that maxima 1051 and 1052 should belong tothe same object. In the above example, this may be indicated bydistOffset meeting or exceeding the distance d between the maxima.Otherwise, operations return to 1010, and convexity of regions A and Bare re-calculated based on the updated parameter(s) of region C.

Once the associations for every maximum are determined, the determinedassociations may be stored, for example in a matrix (also referred to asan association matrix). The stored information may be used to reduce thefull list of maxima to a final list of candidate objects. For instance,in the case of an association matrix, a master list may be created fromthe full list of maxima, and then each maximum may be iterativelyreviewed and removed from the master list if an associated maximum stillremains on the list.

In the example of FIGS. 9 and 10, the time t_(x) at which the secondimage is taken (and, thus, the earliest time that the routine 900 may beexecuted) may be only a few hours into the incubation process. Such atime is generally considered too early to identify fully formedcolonies, but may be sufficient for creating a segmentation image. Thesegmentation image may optionally be applied to future images which aretaken at a subsequent time. For example, boundaries between colonies maybe drawn to predict an expected growth of the colonies. Then, in a caseof confluence among the colonies, the boundaries may be utilized toseparate confluent colonies.

Analysis with Two or More Images after Time T₀

While the above described processes and routines require only one imagetaken after time t₀ (e.g., a first digital image at time t₀ and a seconddigital image at time t_(x)), other processes require at least a secondimage taken after time t₀. For example, if it is discovered that theimage at time t_(x) includes confluent colonies, another image taken attime t_(n) (in which 0<n<x) may be used to identify and split up theindividual colonies.

For instance, if t₀=0 hours into incubation (at which time no growth hasoccurred) and t_(x)=24 hours into incubation (at which time so muchgrowth has occurred that colonies are now confluent), an image at timet_(n)=12 hours (at which time the colonies would have begun to grow butnot yet be confluent) would reveal the presence of individual colonies.Colony growth could then be projected based on the image at time t_(n)to estimate boundaries between the confluent colonies at time t_(x). Inthis regard, the image at time t_(n) could help to differentiate a fastgrowing colony from a slow growing colony. Those skilled in the artshould recognize that as the number of images taken between time t₀ andtime t_(x) increases, the more accurately the growth rate of thecolonies may be projected.

In one application of the foregoing concept, the image taken at timet_(n) (or more generally, images taken between times t₀ and t_(x)) couldbe used to identify colony seeds, which are objects suspected of beingcolonies that will grow over time, and associate the seeds withcorresponding masks and labels. Each seed would receive a unique labeland the label would be stored along with different features (e.g.,position, morphological, and histogram based on images generated fromSHQI images: red channel, green channel, blue channel, luminance,chrominance, hue or composite image) and properties (e.g.,isolated/non-isolated status, other information for projectingchronological propagation). Some stored features (e.g., histogram) mayalso be computed at the plate level, instead of being attributed to aspecific seeds, in order to extract plate global indicators. The seedsstored features could then be used to perform colony extraction at timet_(x), as well as being provided as input to the classifiers fortraining and/or testing.

Growth rate tracking using multiple images taken after to could also beused to detect dust, artifacts, or other foreign objects which appear onthe plate or in the imaging lens in the middle of the workflow routine.For instance, if a speck of dust were to land on the imaging lens afterto but before t_(n), the spot created by the speck could initially beinterpreted as a growing colony since it was not visible at time t₀.However, with subsequent imaging revealing no change in size to thespot, it may be determined that the spot is not growing, and thereforenot a colony.

Aside from tracking growth rate and segmentation, other aspects of thecolonies may be tracked with the help of additional images between t₀and t_(x). In the case of subtle morphological changes that develop in acolony slowly over time, those subtle changes could be identifiedquicker by capturing more images. In some cases, growth could bemeasured along a z-axis, in addition to or instead of along the usual x-and y-axes. For instance, Streptococcus pneumonia is known to slowlyform a sunken center when grown in blood agar, but the sunken center isgenerally not visible until the second day of analysis. By looking at atime progression of the bacteria growth, an incipient sinking center maybe detected and the bacteria identified much earlier than if one mustwait for the center to completely sink.

In other cases, a colony could be known to change color over time.Therefore, imaging of a colony having a first color (e.g., red) at atime after t₀, and then having a second color (e.g., green) at asubsequent time, could be used to determine the identity of the bacteriagrowing in the colony. Color change could be measured as a vector orpath through color space (e.g., RGB, CMYK, etc.) Changes to otherchromatic features of the colony could be similarly measured.

Object Features

As discussed above in connection with FIG. 4, features of an object onan imaged plate may be characterized as part of the image analysisperformed on the imaged plate. The characterized features may includeboth static features (pertaining to a single image) and dynamic image(pertaining to a plurality of images).

Static features aim at reflecting object attributes and/or surroundingbackground at a given time. Static features include the following:

Center of gravity: this is a static feature that provides a center ofgravity of an imaged object in a coordinate space (e.g., x-y, polar).The center of gravity of an object, like the polar coordinates of theobject, provides invariance in the feature set under given lighting andbackground conditions. The center of gravity may be obtained by firstdetermining a weighted center of mass for all colonies in the image (Mbeing the binary mask of all detected colonies). The weighted center ofmass may be determined based on an assumption that each pixel of theimage is of equal value. The center of gravity for a given colony maythen be described in x-y coordinates by the following equation (in whichE={p|p∈M} (E is the current colony's binary mask), the range for thex-coordinate is [0, image width], the range for the y-coordinate is [0,image height], and each pixel is one unit):

$\begin{matrix}{ig{v_{({x,y})}( {{x = {\frac{1}{\sum_{p \in E}1} \times {\sum_{p \in E}p_{x}}}},{y = {\frac{1}{\sum_{p \in E}1} \times {\sum_{p \in E}p_{y}}}}} )}} & (9)\end{matrix}$

(ii) Polar coordinates: this is also a static feature, and can be usedto further characterize locations on the imaged plate, such as a centerof gravity. Generally, polar coordinates are measured along a radialaxis (d) and an angular axis (θ), with the coordinates of the platecenter being [0,0]. Coordinates d and θ of igv_((x,y)) are given (inmillimeters for d, and in degrees for θ) by for following equations(Where k is a pixel density corresponding pixels to millimeters, and“barcode” is a landmark feature of the imaged plate to ensure alignmentof the plate with previous and/or future images):d=k×dist(igv _((x,y)),0_((x,y)))  (10)θ=Angle(barcode,0_((x,y)) ,igv _((x,y)))  (11)

(iii) Image vector: The two-dimensional polar coordinates may in turn betransformed into a one-dimensional image vector. The image vector maycharacterize intensity of the pixels of an image as a function of theradial axis (generally, with the center of the colony having the highestintensity) and/or a function of the angular axis. In many cases, theimage vector may be more accurate at classifyingsimilarities/distinctions among imaged objects.

(iv) Morphometric features, which describe the shape and size of a givenobject.

-   -   (a) Area: This is a morphometric feature, and can be determined        based on the number of pixels in the imaged object (also        referred to as a “blob”), not counting holes in the object. When        pixel density is available, area may be measured in physical        size (e.g., mm²). Otherwise, when pixel density is not        available, the total number of pixels may indicate size, and        pixel density (k) is set to equal one. In one embodiment, area        is calculated using the following equation:        A=k ²×Σ_(p∈E)1  (12)    -   (b) Perimeter: The perimeter of the object is also a        morphometric feature, and can be determined by measuring the        edges of the objecting and adding together the total length of        the edges (e.g., a single pixel having an area of 1 square unit        has a perimeter of 4 units). As with area, length may be        measured in terms of pixel units (e.g., when k is not available)        or physical lengths (e.g., when k is available). In some        circumstances, the perimeter may also include the perimeter of        any holes in the object. Additionally, the ladder effect (which        results when diagonal edges are digitized into ladder-like        boxes) may be compensated by counting inside corners as √{square        root over (2)}, rather than 2. In one embodiment, perimeter may        be determined using the following equations:

$\begin{matrix}{P = {k \times {\sum_{p \in E}{q( n_{p} )}}}} & (13) \\{n_{p} = \begin{Bmatrix}\; & t & \; \\l & p & r \\\; & b & \;\end{Bmatrix}} & (14) \\{{{if}\text{:}\mspace{14mu}\{ {{{\sum( {{t \in M},{l \in M},{r \in M},{b \in M}} )} = 2},( {l \in {M \neq r} \in M} ),( {t \in {M \neq b} \in M} )} \}}( {p\mspace{14mu}{is}\mspace{14mu}{interior}\mspace{14mu}{and}\mspace{14mu} p\mspace{14mu}{is}\mspace{14mu} a\mspace{14mu}{corner}} ){{{then}\text{:}\mspace{14mu}{q( n_{p} )}} =  \sqrt{}2 }{{{else}\text{:}\mspace{14mu}{q( n_{p} )}} = {4 - {\sum( {{t \in M},{l \in M},{r \in M},{b \in M}} )}}}} & (15)\end{matrix}$

-   -   (c) Circularity: The circularity of the object is also a        morphometric feature, and can be determined based on a        combination of the area and perimeter. In one embodiment,        circularity is calculated using the following equation:

$\begin{matrix}{C = \frac{4\pi A}{p^{2}}} & (16)\end{matrix}$

-   -   (d) Radius Coefficient of Variation (RCV): This is also a        morphometric feature, and is used to indicate variance in radius        of the object by taking a ratio between the mean radius R of the        object in all N directions or angles θ extending from the center        of gravity and standard deviation of the radii σ_(R). In one        embodiment, this value can be calculated using the following        equations:

$\begin{matrix}{\overset{\_}{R} = \frac{\sum_{\theta = 0}^{2\pi}R_{\theta}}{N_{\theta}}} & (17) \\{\sigma_{R} = \sqrt{\frac{\sum_{\theta = 0}^{2\pi}( {R_{\theta} - \overset{¯}{R}} )^{2}}{N_{\theta} - 1}}} & (18) \\{{RCV} = \frac{\sigma_{R}}{R}} & (19)\end{matrix}$

(v) Contextual features, which describe the neighborhood topographicalrelationships of the object under scrutiny to the other detected objectsand plate walls edges. For example, in the case of an imaged colony, onecontextual feature of the colony may be whether the colony is free, haslimited free space, or is competing for access to resources with othersurrounding colonies. Such features tend to help classify coloniesgrowing in the same perceived environment, and/or discriminatingcolonies growing in different environments.

-   -   (a) Region of Influence: this is a contextual feature that        considers the space between an object and its neighboring        objects and predicts a region that the object under analysis may        expend to occupy (without other, different objects expending to        occupy that same region first). The region of influence can be        expressed in the form of a Voronoï diagram, such as the diagram        shown in FIG. 12, which shows a region of influence (shaded)        based on the distance d between a colony 1201 and its        neighboring colonies, e.g., 1205. In one embodiment, the        distance from the edge of the object to the edge of the region        of influence (D_(NC)) may be characterized using the following        equation:        D _(NC) =k×Min[dist(p∈E,{acute over (p)}∈M        E)]  (20)    -   (b) Distance to Plate Wall: this is a contextual feature that        calculates the distance of the edge of the object from the        nearest plate wall (D_(PW)). In one embodiment, this distance        may be characterized using the following equation:        D _(PW) =k×Min[dist(p∈E,{acute over (p)}        Plate)]  (21)    -   (c) Isolation Factor: this is a contextual feature        characterizing the relative isolation of a given object based on        the object's size and distance to the nearest edge (e.g., of        another object, a plate wall). FIGS. 13A-C illustrate aspects of        isolation factor. FIG. 13A illustrates an instance in which the        nearest edge is distance d from the colony to a plate wall.        FIGS. 13B and 13C illustrate an instance in which the nearest        edge belongs to another colony. In such a case, a circle is        drawn centered around the colony under analysis and then        expanded (first small, as in FIG. 13B, then larger as in FIG.        13C) until the circle touches a neighboring colony. In the        embodiments of FIGS. 13A-C, the isolation factor (IF) may be        characterized using the following equation:

$\begin{matrix}{{IF} = \frac{{Min}( {D_{NC},D_{PW}} )}{\overset{¯}{R}}} & (22)\end{matrix}$

-   -   (d) Neighboring Occupancy Ratio: this is a contextual feature        characterizing the area fraction of a plate's bounded Voronoï        region of influence (V) within a given distance d for a given        object. In one embodiment, the neighboring occupancy ratio (OR)        may be characterized using the following equation (in which for        this equation,

$\begin{matrix}{{ {E = \{ { p \middle| {p \in V} ,{{{dist}( {p,{{ig}v_{({x,y})}}} )} < d}} \}} )\text{:}}{{{OR}(d)} = \frac{k^{2} \times {\sum_{p \in E}1}}{{\pi( {d/2} )}^{2}}}} & (23)\end{matrix}$

-   -   (e) Relative Neighboring Occupancy Ratio: in some instances, the        given distance d may be derived using the mean radius of the        object multiplied by a predetermined factor (d=x×R). The result        is a relative neighboring occupancy ratio (RNOR), and may be        derived for a given factor x using the following equations:        RNOR(x)=NOR(d)  (24)

(vi) Spectral features, which describe the light properties of a givenobject. Color (red, green, and blue light channels; hue, luminance andchrominance, or any other color space transformation), texture andcontrast (over time and/or across space) are examples of such features.Spectral features can be derived from images captured at various timepoints and/or under various illumination conditions during incubationusing colony masks, and can further be associated with a Voronoï regionof influence for a given colony.

-   -   (a) Channel Image: this is a spectral feature in which a        specific color channel (e.g., red (R), green (G), blue (B)) is        used to spectrally resolve the image.    -   (b) Luma: this is also a spectral feature used to characterize        brightness of an image using RGB channels as an input.    -   (c) Hue: this is a spectral feature in which an area of the        image is characterized as appearing to be similar to a perceived        color (e.g., red, yellow, green, blue) or a combination thereof.        Hue (H₂) is generally characterized using the following        equations:

$\begin{matrix}{H_{2} = {{atan}\; 2( {\beta,\alpha} )}} & (25) \\{\alpha = {R - {\frac{1}{2}( {G + B} )}}} & (26) \\{\beta = {\frac{ \sqrt{}3 }{2}( {G - B} )}} & (27)\end{matrix}$

-   -   (d) Chroma: this is a spectral feature for characterizing the        colorfulness of an area of an image relative to its brightness        if that area were similarly illuminated white. Chroma (C₂) is        generally characterized using the following equation:        C ₂√{square root over (α²)}+β²  (28)    -   (e) Radial Dispersion: analyzing radial dispersion of the hue        and chroma contrast enables the discrimination of alpha, beta        and gamma hemolysis.    -   (f) Maximum Contrast: this feature characterizes resolution of        the image by computing the maximum of a measured average        contrast for pixels at a given radius r from a central point of        the image (e.g., the center of an imaged colony). This feature        may be used to describe the perceptual differences between an        image taken at times t₀ and t_(x) based on the growth induced by        the analyzed object. Maximum contrast may be characterized as        follows:        MaxContrast_(r)=MAX(AverageContrast_(r))_(object)  (29)

(vii) Background features, which describe alterations in the media inthe neighborhood of the analyzed object. For instance, in the case of animaged colony, the changes could be caused by microbial growth aroundthe colony (e.g., signs of hemolysis, changes in PH, or specificenzymatic reactions).

Dynamic features aim at reflecting a change of object attributes and/orsurrounding background over time. Time series processing allows staticfeatures to be related over time. Discrete first and second derivativesof these features provide instantaneous “speed” and “acceleration” (orplateauing or deceleration) of the change in such features to becharacterized over time. Examples of dynamic features include thefollowing:

(i) Time series processing for tracking the above static features overtime. Each feature measured at a given incubation time may be referencedaccording to its relative incubation time t₀ allows for the features tobe related ones measured at later incubation times. A time series ofimages can be used to detect objects such as CFUs appearing and growingover time, as described above. Time points for imaging may be preset ordefined by an automated process based upon ongoing analysis ofpreviously captured images of the objects. At each time point the imagecan be a given acquisition configuration, either for the entire seriesof a single acquisition configuration, or as a whole series of imagescaptured from multiple acquisition configurations.

(ii) Discrete first and second derivatives of the above features forproviding instant speed and acceleration (or plateauing or deceleration)of the changes to such features over time (e.g., tracking growth rate,as discussed above):

-   -   (a) Velocity: a first derivative of a feature over time.        Velocity (V) of a feature x may be characterized in terms of (x        units)/hour, with Δt being a span of time expressed in hours,        based on the following equations:

$\begin{matrix}{V = {\lim\limits_{{\Delta t}arrow 0}( \frac{dx}{dt} )^{n}}} & (30) \\{V_{1,0} = \frac{x_{1} - x_{0}}{t_{1} - t_{0}}} & (31) \\{V_{2,1} = \frac{x_{2} - x_{1}}{t_{2} - t_{1}}} & (32)\end{matrix}$

-   -   (b) Acceleration: a second derivative of the feature over time,        also the first derivative of Velocity. Acceleration (A) may be        characterized based on the following equation:

$\begin{matrix}{A = \ {\lim\limits_{{\Delta t}arrow 0}\frac{dV}{dt}}} & (33)\end{matrix}$

The above image features are measured from the objects or the objects'context and aim at capturing specificities of organisms growing onvarious media and incubation conditions. The listed features are notmeant to be exhaustive and any knowledgeable person in the field couldmodify, enlarge or restrict this feature set according to the variety ofknown image processing based features known in the field.

Image features may be collected for each pixel, group of pixels, object,or group of objects, in the image. A distribution of the collectedfeatures can be constructed in a histogram in order to more generallycharacterize regions of the image, or even the entire image. Thehistogram can itself rely on several statistical features in order toanalyze or otherwise process the incoming image feature data.

Statistical histogram features can include the following:

(i) Minimum: the smallest value of the distribution captured within thehistogram. This may be characterized by the following relationship:Min=i|{h(i)>0,Σ_(j=0) ^(j<i) h(i)=0}  (34)

(ii) Maximum: the largest value of the distribution captured within thehistogram. This may be characterized according to the followingrelationship:Max=i|{h(i)>0,Σ_(j=i+1) ^(∞*) h(i)=0}  (35)

(iii) Sum: the sum of all the individual values captured within thehistogram. Sum may be defined by the following relationship:Sum=Σ_(i=min) ^(max) i×h(i)  (36)

(iv) Mean: the arithmetic mean, or average. This is the sum of all thescores divided by the number of scores (N) according to the followingrelationship:

$\begin{matrix}{{Mean} = \frac{\sum_{i = {m\; i\; n}}^{m\;{ax}}{i \times {h(i)}}}{N}} & (37)\end{matrix}$

(v) Quartile 1 (Q1): The score at the 25^(th) percentile of thedistribution. 25% of the scores are below Q1 and 75% are above Q1. Thisis described by the following relationship:

$\begin{matrix}{Q_{1} =  i \middle| \{ {{{\sum_{j = \min}^{j < 1}{h(i)}} < \frac{N}{4}},{{\sum_{j = \min}^{j \leq i}{h(i)}} \geq \frac{N}{4}}} \} } & (38)\end{matrix}$

(vi) Median (Q2): The score at the 50^(th) percentile of thedistribution. 50% of the scores are below the median and 50% are abovethe median. The median is less sensitive to extreme scores than the meanand this generally makes it a better measure than the mean for highlyskewed distributions. This is described by the following relationship:

$\begin{matrix}{{Median} = {Q_{2} =  i \middle| \{ {{{\sum_{j = \min}^{j < 1}{h(i)}} < \frac{N}{2}},{{\sum_{j = \min}^{j \leq i}{h(i)}} \geq \frac{N}{2}}} \} }} & (39)\end{matrix}$

(vii) Quartile 3 (Q3): The score at the 75th percentile of thedistribution. 75% percent of the scores are below Q3 and 25% are aboveQ3. This is described by the following relationship:Q ₃ =i|{Σ _(j=min) ^(j<i) h(i)<¾N,Σ _(j=min) ^(j≤i) h(i)≥¾N}  (40)

(viii) Mode: The most frequently occurring score in a distribution. Thisis used as a measure of central tendency. The advantage of the mode as ameasure of central tendency is that its meaning is obvious. Further, itis the only measure of central tendency that can be used with nominaldata. The mode is highly subject to sample fluctuations and is thereforegenerally not used as the only measure of central tendency. Also, manydistributions have more than one mode. These distributions are called“multimodal.” Mode is described by the following relationship:Mode=i|{h(i)≥h(i)_(i=min) ^(max)}  (41)

(ix) Trimean: A score computed by adding the 25th percentile plus twicethe 50th percentile (median) plus the 75th percentile and dividing byfour. The trimean is almost as resistant to extreme scores as the medianand is less subject to sampling fluctuations than the arithmetic mean inskewed distributions. However, it is generally less efficient than themean for normal distributions. Trimean is described according to thefollowing relationship:

$\begin{matrix}{{TriMean} = \frac{Q_{1} + {2Q_{2}} + Q_{3}}{4}} & (42)\end{matrix}$

(x) Trimmed mean: A score calculated by discarding a certain percentageof the lowest and the highest scores and then computing the mean of theremaining scores. For example, a mean trimmed 50% is computed bydiscarding the lower and higher 25% of the scores and taking the mean ofthe remaining scores. For further example, the median is the meantrimmed 100% and the arithmetic mean is the mean trimmed 0%. The trimmedmean is generally less susceptible to the effects of extreme scores thanis the arithmetic mean. It is therefore less susceptible to samplingfluctuation than the mean for skewed distributions. It is generally lessefficient than the mean for normal distributions. By way of example, themean trimmed 50% is described by the following relationship:

$\begin{matrix}{{TrimmedMean}_{50} = \frac{\sum_{i = Q_{1}}^{Q_{3}}{i \times {h(i)}}}{\sum_{i = Q_{1}}^{Q_{3}}{h(i)}}} & (43)\end{matrix}$

(xi) Range: The difference between the largest and the smallest values.The range can be a useful measure of spread. However, it is sensitive toextreme scores since it is based on only two values. Due to thissensitivity, the range is generally not used as the only measure ofspread, but can nonetheless be informative if used as a supplement toother measures of spread such as standard deviation orsemi-interquartile range.

(xii) Semi-interquartile range: A measure of spread computed as one-halfthe difference between the 75th percentile (Q3) and the 25th percentile(Q1). Since half of the scores in a distribution lie between Q3 and Q1,the semi-interquartile range is half the distance needed to cover saidhalf of the scores. In a symmetric distribution, an interval stretchingfrom one semi-interquartile range below the median to onesemi-interquartile above the median will contain half of the scores.This is not true for a skewed distribution, however. Unlike range,semi-interquartile range is generally not substantially affected byextreme scores. However, it is more subject to sampling fluctuation innormal distributions than is standard deviation, and therefore is notoften used for approximately normally distributed data.Semi-interquartile range is defined according to the followingrelationship:

$\begin{matrix}{{SemiInterQuartilRange}{= \frac{Q_{3} - Q_{1}}{2}}} & (44)\end{matrix}$

(xiii) Variance: A measure of distribution spread. Variance iscalculated by taking the average squared deviation of each number fromits mean, according to the following relationship:

$\begin{matrix}{{Variance} = {\frac{1}{n - 1}{\sum_{i = \min}^{\max}{{h(i)} \times ( {i - {Mean}} )^{2}}}}} & (45)\end{matrix}$

(xiv) Standard deviation: A function of variance that measures howwidely the values of a distribution are dispersed from the mean.Standard deviation is the square root of the variance. Althoughgenerally less sensitive to extreme scores than the range, standarddeviation is generally more sensitive than semi-interquartile range.Thus, semi-interquartile range may be used to supplement standarddeviation when the possibility of extreme scores exists.

(xv) Skewness: A measure of a distribution's asymmetry around its mean.A distribution is skewed if one of its tails is longer than the other.Positive skewness indicates a distribution with an asymmetric tailextending toward more positive values (greater than the mean). Negativeskewness indicates a distribution with an asymmetric tail extendingtoward more negative values (less than the mean). Skewness may becalculated according to the following relationship:

$\begin{matrix}{{Skew}{= {\frac{N}{( {N - 1} ) \times ( {N - 2} )}{\sum_{i = \min}^{\max}( {{h(i)} \times ( \frac{i - {Mean}}{StdDev} )^{3}} )}}}} & (46)\end{matrix}$

(xvi) Kurtosis: A measure of steepness or flatness of a distribution (ora relative peak width), as compared to a normal distribution. Positivekurtosis indicates a relatively peaked distribution. Negative kurtosisindicates a relatively flat distribution. Kurtosis is based on the sizeof a distribution's tails and is determined by the followingrelationship:

$\begin{matrix}{{Kurtosis} = {{\frac{N \times ( {N + 1} )}{( {N - 1} ) \times ( {N - 2} ) \times ( {N - 3} )}{\sum_{i = \min}^{\max}( {{h(i)}\  \times ( \frac{i - {Mean}}{StdDev} )^{4}} )}} - {3\frac{( {N - 1} )^{2}}{( {N - 2} ) \times ( {N - 3} )}}}} & (47)\end{matrix}$

The above statistical methods are useful for analyzing spatialdistributions of grey values, by computing local features at each pointin the image, and deriving a set of statistics from the distributions ofthe local features. With these statistical methods, textures for theanalyzed regions can be described and statically defined.

Texture can be characterized using texture descriptors. Texturedescriptors can be computed over a given region of the image (discussedin greater detail below). One commonly applied texture method is theco-occurrence method, introduced by Haralick, R., et al. “Texturefeatures for image classification,” IEEE Transactions of System, Man andCybernetics, Vol. 3, pp. 610-621 (1973), which is incorporated byreference herein. In this method, the relative frequencies of grey levelpairs of pixels separated by a distance d in the direction θ arecombined to form a relative displacement vector (d, θ). The relativedisplacement vector is computed and stored in a matrix, referred to asgrey level co-occurrence matrix (GLCM). This matrix is used to extractsecond-order statistical texture features. Haralick suggests fourteendifferent features to describe a two dimensional probability densityfunction pij, four of which features are more commonly used than theothers:

Texture can be characterized using texture descriptors. Texturedescriptors can be computed over a given region of the image (discussedin greater detail below). One commonly applied texture method is theco-occurrence method, introduced by Haralick, R., et al. “Texturefeatures for image classification,” IEEE Transactions of System, Man andCybernetics, Vol. 3, pp. 610-621 (1973), which is incorporated byreference herein. In this method, the relative frequencies of grey levelpairs of pixels separated by a distance d in the direction θ arecombined to form a relative displacement vector (d, θ). The relativedisplacement vector is computed and stored in a matrix, referred to asgrey level co-occurrence matrix (GLCM). This matrix is used to extractsecond-order statistical texture features. Haralick suggests fourteendifferent features to describe a two dimensional probability densityfunction pij, four of which features are more commonly used than theothers:

Angular Second Moment (ASM) is calculated by the following:ASM=Σ_(i=0) ^(N−1)Σ_(j=0) ^(N−1)p_(i) ² _(j)  (48)

(ii) Contrast (Con) is calculated by the following:Con=Σ_(i=0) ^(N−1)Σ_(j=0) ^(N−1)(i−j)² p _(ij)  (49)

(iii) Correlation (Cor) is calculated by the following (in which σ_(x)and σ_(y) are standard deviations of the corresponding distributions):

$\begin{matrix}{{Cor} = {\frac{1}{\sigma_{x}\sigma_{y}}{\sum_{i = 0}^{N - 1}{\sum_{j = 0}^{N - 1}{p_{ij}{\log( p_{ij} )}}}}}} & (50)\end{matrix}$

(iv) Entropy (Ent) is calculated by the following:Ent=Σ_(i=0) ^(N−1)Σ_(j=0) ^(N−1) p _(ij) log (p _(ij))  (51)

These four features are also listed in Strand, J., et al. “Localfrequency features for the texture classification,” Pattern Recognition,Vol. 27, No. 10, pp 1397-1406 (1994) [Strand94], which is alsoincorporated by reference herein.

For a given image, the region of the image over which the above featuresare evaluated may be defined by a mask (e.g., a colony mask), or by aVoronoï region of influence extending beyond the mask.

FIG. 14 illustrates a few possible regions. Region 1410 is a colony maskthat extends only as far as the colony itself. Region 1420 is theVoronoï region of influence of the colony (bounded by the edge of theimage or the plate). For further illustration, pixel 1430 is a pixelwithin region 1420 but outside of region 1410. In other words, thecolony represented by region 1410 is expected to expand into pixel 1630but has not done so yet. Pixel 1440 is a pixel outside of both regions1410 and 1420. In other words, not only does the colony not occupy pixel1410 at the time of the image, but it is not predicted to be occupied bythe colony any time in the future either (in this case, it is alreadyoccupied by a different colony).

Using colony masks at the different time points along the incubationprocess and their associated Voronoï regions of influence as describedabove, it is possible to generate multiple histograms depictingdifferent aspects of the colonies and their impact on local surroundinggrowing media. The colony masks and Voronoï regions of influencethemselves may be adjusted over time, for instance as the colonies grow.For example, FIGS. 15A-C illustrate how growth of a colony can be usedto adjust a mask of the colony over time. FIG. 15A is a portion of ablood culture after 24 hours of growth in agar. FIG. 15B is a contrastimage of the same culture, as compared to an image previously capturedat t0. FIG. 15C is a grey scale image illustrating growth at 9 hours(lightest), 12 hours (medium) and 24 hours (darkest). Each shade in FIG.15C could be used to design a different mask for the colony.Alternatively or additionally, as growth occurs, the masks could beseparated according to their respective Voronoï regions of influence.

Any one or combination of features in the above list of features may beused as a feature set for capturing specificities of organisms growingon various media of an imaged plate under various incubation conditions.This list is not meant to be exhaustive, and anyone knowledgeable in thefield could modify, enlarge or restrict this feature set according tothe intended objects to be imaged and the variety ofimage-processing-based features known in the field. Thus, the examplefeatures above are offered by way of illustration, not limitation.

Those skilled in the art are aware of other measurements and approachesto determine object shapes and features, and the examples above areoffered by way of illustration, not limitation.

Contrast Building

It is often difficult to predict initially which image in an imageseries will bring values for growth detection, counting oridentification. This is in part because image contrast varies for thedifferent colony forming units (CFUs) and across different media. In agiven image of several colonies, one colony may have highly desirablecontrast with the background while another colony may not have adequatecontrast with the background for growth detection. This also makes itdifficult to use a single approach to identify colonies on media.

It is therefore desirable to build contrast from all available materialthrough space (spatial differences) and time (temporal differences undercommon imaging conditions), as well as by using various imagingconditions (e.g., red, green and blue channels, light and darkbackgrounds, spectral images or any other color space transformation).It is also desirable to gather contrast from multiple available sourcesto provide a standardized image as an input to an algorithm fordetecting colonies.

Image data can be delimited based upon any number of factors. Forexample, image data can be limited to particular time points and/orparticular information sought (e.g. spatial image information may notrequire as many time points as temporal image information requires).Illumination configurations and color spaces can also be selected toachieve specific contrast objectives. Spatial frequencies can also bevaried in order to detect objects (e.g., colonies) having a desired size(or size within a target range).

To detect discrete objects, contrast can be set to absolute values on[0,1] or signed [−1,−1]. A scale and an offset of the contrast outputcan also be specified (e.g., for 8 bits image with signed contrastoffset can be 127.5 and the scale can be 127.5). In an example where thecontrast is set to an extreme, the absolute offset may set to zero andthe scale to 256.

Spatial contrast may be used to detect discrete objects on a homogeneousbackground. A formula may be utilized to provide automated evaluation ofspatial contrast C_((x,y)) ^(I,r) on an image I at location (x, y)within distance r. In one embodiment, in which distance r is limited todistances greater than or equal to √{square root over((x_(i)−x)²+(y_(i)−y)²)}, and a contrast operator K is used to controlcontrast settings, the following equations are applied:

$\begin{matrix}{C_{({x,y})}^{I,r} = \frac{{I_{({x,y})} - K_{({x_{i},y_{i,r}})}}}{I_{({x,y})} + K_{({x_{i},y_{i,r}})}}} & (52) \\{K_{({x_{i},y_{i,r}})} = {\frac{1}{\pi\; r^{2}}{\sum_{r}I_{({x_{i},y_{i}})}}}} & (53)\end{matrix}$

Temporal contrast may be used to detect moving objects or objectschanging over time (such as CFUs appearing and or expending on an imagedplate). A formula may be utilized to provide automated evaluation oftemporal contrast C_((x,y)) ^(I(T) ^(x) ^(,T) ⁰ ⁾ on an image I atlocation (x, y) between times t0 and tx. In one embodiment, thefollowing equation is applied:

$\begin{matrix}{C_{({x,y})}^{I{({t_{x},t_{0}})}} = \frac{{I_{({x,y})}^{t_{x}} - I_{({x,y})}^{t_{0}}}}{I_{({x,y})}^{t_{x}} + I_{({x,y})}^{t_{0}}}} & (54)\end{matrix}$

Spatial contrast gathering can be implemented in an automated fashion bygenerating a plurality of SHQI images of a plate according to apre-programmed sequence. Multiple images would be generated at a givenincubation time t₀ further colony detection investigations. In oneembodiment, in which image data (particularly, a vector “vect” used toprovide contrast inputs to the contrast gathering operator) is collectedover several configurations (CFG1 through CFGN) at several differentradii from the detected colony (Rmin through Rmax) at a given timeaccording to the following:

$\begin{matrix}{C_{({x,y})}^{({R_{\min},R_{\max}})} = {{\,_{({R_{\min},R_{\max}})}^{{vect}{\langle{{CFG}_{1},\ldots\mspace{14mu},{CFG}_{N}}\rangle}}{Max}}( \frac{{I_{({x,y})} - K_{({x_{i},y_{i,r}})}}}{I_{({x,y})} + K_{({x_{i},y_{i,r}})}} )}} & (55)\end{matrix}$

If SNR is known for a given image I_((x,y)) (e.g., when SHQI imaging isthe source), the configuration in which SNR weighted contrast ismaximized may be identified as the best configuration (Best CFG) when:

$\begin{matrix}{{\,_{({R_{\min},R_{\max}})}^{CFG}( \frac{{I_{({x,y})} - K_{({x_{i},y_{i,r}})}}}{I_{({x,y})} + K_{({x_{i},y_{i,r}})}} )}*{SNR}_{({x,y})}\mspace{14mu}{is}\mspace{14mu}{maximum}\mspace{14mu}{over}\text{:}} & (56) \\{{\,_{({R_{\min},R_{\max}})}^{{vect}{\langle{{CFG}_{1},\ldots\mspace{14mu},{CFG}_{N}}\rangle}}( \frac{{I_{({x,y})} - K_{({x_{i},y_{i,r}})}}}{I_{({x,y})} + K_{({x_{i},y_{i,r}})}} )} = {SNR}_{({x,y})}} & (57)\end{matrix}$

The contrast operator K further benefits from this known SNR informationand the above equation becomes:

$\begin{matrix}{C_{({x,y})}^{({R_{\min},R_{\max}})} = {\,_{\;}^{{\,_{({R_{\min},R_{\max}})}^{{vect}{\langle{{CFG}_{1},\ldots\mspace{14mu},{CFG}_{N}}\rangle}}{Best}}\mspace{14mu}{CFG}}( \frac{{I_{({x,y})} - K_{({x_{i},y_{i,r}})}}}{I_{({x,y})} + K_{({x_{i},y_{i,r}})}} )}} & (58)\end{matrix}$

Temporal contrast gathering can also be implemented in an automatedfashion by generating a plurality of SHQI images of a plate according toa pre-programmed sequence. Multiple images would be generated overmultiple incubation times, at least one of which is t0, to furthercolony detection investigations. In one embodiment, image data iscollected over several configurations at time t0 and one or moresubsequent incubation times up to time tx according to the following:

$\begin{matrix}{C_{({x,y})}^{{vect}{\langle{t_{0},\ldots\mspace{14mu},t_{x}}\rangle}} = {{\,^{{vect}{\langle{{CFG}_{1},\ldots\mspace{14mu},{CFG}_{N}}\rangle}}{Max}}( \frac{{I_{({x,y})}^{{}_{}^{}{}_{}^{}} - I_{I_{({x,y})}}^{t_{0}}}}{I_{({x,y})}^{{}_{}^{}{}_{}^{}} + I_{I_{({x,y})}}^{t_{0}}} )}} & (59)\end{matrix}$

In the above example, the vector may be a vector between two time points(e.g., t0 and tx) based upon differences in the images at those twotimes. However, in other applications, in which additional times betweent0 and tx are included, the vector may be mapped over as many points asthere are times at which images are taken. Mathematically speaking,there is no limit to the number points that may be included in vector.

As with spatial contrast, if SNR is known for a given image I_((x,y)),the configuration in which SNR weighted contrast is maximized may beidentified as the best configuration (Best CFG) when:

$\begin{matrix}{{\,_{\;}^{CFG}( \frac{{I_{({x,y})}^{{}_{}^{}{}_{}^{}} - I_{I_{({x,y})}}^{t_{0}}}}{I_{({x,y})}^{{}_{}^{}{}_{}^{}} + I_{I_{({x,y})}}^{t_{0}}} )}*{SN}R_{({x,y})}\mspace{14mu}{is}\mspace{14mu}{maximum}\mspace{14mu}{over}} & (60) \\{{\,_{\;}^{{vect}{\langle{{CFG}_{1},\ldots\mspace{14mu},{CFG}_{N}}\rangle}}( \frac{{I_{({x,y})}^{{}_{}^{}{}_{}^{}} - I_{I_{({x,y})}}^{t_{0}}}}{I_{({x,y})}^{{}_{}^{}{}_{}^{}} + I_{I_{({x,y})}}^{t_{0}}} )}*{SNR}_{({x,y})}} & (61)\end{matrix}$

The contrast operator K further benefits from this known SNR informationand the above equation becomes:

$\begin{matrix}{C_{({x,y})}^{{vect}{\langle{t_{0},{\ldots\mspace{14mu} t_{x}}}\rangle}} = {\,_{\;}^{{\,^{{vect}{\langle{{CFG}_{1},\ldots\mspace{14mu},{CFG}_{N}}\rangle}}{Best}}\mspace{14mu}{CFG}}( \frac{{I_{({x,y})}^{{}_{}^{}{}_{}^{}} - I_{I_{({x,y})}}^{t_{0}}}}{I_{({x,y})}^{{}_{}^{}{}_{}^{}} + I_{I_{({x,y})}}^{t_{0}}} )}} & (62)\end{matrix}$

In the above examples, the Max operator could be replaced by any otherstatistical operator such as a percentile (e.g., Q1, median, Q3, or anyother percentile) or weighted sum. Weighted values could originate frompre-work extracted from a training database, thereby opening the fieldof supervised contrast extraction to neural networks. Additionally,multiple algorithms may be used, with the results of the multiplealgorithms being further combined using another operator, such as theMax operator.

Image Alignment

When multiple images are taken over time, very precise alignment ofimages is needed in order to obtain valid temporal estimations fromthem. Such alignment can be achieved by way of a mechanical alignmentdevice and/or algorithms (e.g., image tracking, image matching). Thoseknowledgeable in the field are cognizant of these solutions andtechniques to achieve this goal.

For instance, in cases where multiple images of an object on the plateare collected, the coordinates of an object's location may bedetermined. Image data of the object collected at a subsequent time maythen be associated with the previous image data based on thecoordinates, and then used to determine the change in the object overtime.

For rapid and valuable usage of images (e.g., when used as input toclassifiers), it is important to store the images in a spatial referenceto maximize their invariance. As the basic shape descriptor for a colonyis generally circular, a polar coordinate system can be used to storecolony images. The colony center of mass may be identified as the centerof the location of the colony when the colony is first detected. Thatcenter point may later serve as origin center for a polar transform ofeach subsequent image of the colony. FIG. 16A shows a zoomed portion ofan imaged plate having a center point “O.” Two rays “A” and “B”extending from point “O” are shown (for purposes of clarity) overlaid onthe image. Each ray intersects with a respective colony (circled). Thecircled colonies of FIG. 16A shown in even greater detail in the images1611 and 1612 FIG. 16B. In FIG. 16B, image 1611 (the colony intersectingray “A”) is reoriented into image 1613 (“A′”) such that the radial axisof image 1613 is aligned with that of image 1612, such that the leftmostpart of the reoriented image is closest to point “O” of FIG. 16A, andthe rightmost part of the reoriented image is farthest from point “O.”This polar reorientation allows for easier analysis of the differentlyoriented (with respect to such factors as illumination) colonies of animaged plate.

In FIG. 16C, a polar transform is completed for each of the images 1611,1612 and 1613 of FIG. 16B. In the polar transform images 1621, 1622 and1623, the radial axis of the respective reoriented images 1611, 1612 and1613 (extending from the center of each respective imaged colony) areplotted from left to right in the images of FIG. 16C, and the angularaxis (of the respective colonies) is plotted from top to bottom.

For each polar image, summary one-dimensional vector sets can begenerated using, for example, shape features and/or histogram features(e.g., average and/or standard deviation of color or intensity of anobject) along the radial and/or angular axis. Even if shape andhistogram features are mostly invariant when considering rotation, it ispossible that some texture features will show significant variationswhen rotated; thus, invariance is not guaranteed. Therefore, there is asignificant benefit to presenting each of the colony images from thesame viewpoint or angle illumination-wise, as the objects' texturedifferences can then be used to discriminate among each other. Asillumination conditions mostly show variations linked to angularposition around a plate imaging center, the ray going through the colonyand plate center (shown as a line in each of images 1611, 1612 and 1613of FIG. 16B) may serve as origin (θ) for each image polar transform.

A further alignment challenge arises from the fact that the plate mediais not absolutely frozen and rigid, and therefore may slightly shiftfrom one taken to the next. Therefore, one cannot absolutely assume thatthe region of a plate at certain coordinates of an image taken at onetime will necessarily perfectly align with the region of the plate atthe same coordinates taken at a later time. Stated another way, slightdeformations of the media may lead to a little uncertainty regarding theexact matching of a given pixel with the corresponding pixel captured ata different time point during the incubation process.

In order to account for this uncertainty, a given pixel intensity valueat time ta, I_((x,y)) ^(t) ^(a) , can be compared with the closestintensity value in the local neighborhood N(x, y) of this pixel at adifferent time point tb, I_(N(x,y)) ^(t) ^(b) . Selection of a localneighborhood may involve determining one or more errors or inaccuraciesin the repositioning of the imaged plate from one time to the next(e.g., a position accuracy error due to imperfect repositioning of theimaged media, a parallax error due to an unknown height of the imagedmedia from one time to the next). In one example, it has been observedthat setting “x” and “y” within the range of about 3 to about 7 pixelsis suitable for an image with resolution of about 50 microns per pixel.

Anyone knowledgeable in the field will recognize as an efficientsolution to generate two tb images from the tb source image: the firstcorresponding to a grey level dilation of tb (referred to as DIL^(t)^(b) ^(,d)), and the second to a grey level erosion of tb, (referred toas ERO^(t) ^(b) ^(,d)), both with a kernel size matching therepositioning distance uncertainty d.

If ERO_((x,y)) ^(t) ^(b) ^(,d)≤I_((x,y)) ^(ta)≤DIL_((x,y)) ^(t) ^(b)^(,d) then the contrast is 0, otherwise the contrast is estimated fromthe closest value to I_((x,y)) ^(T) ^(a) among ERO_((x,y)) ^(t) ^(b)^(,d) and DIL_((x,y)) ^(t) ^(b) ^(,d) using the following:

$\begin{matrix}{C_{({x,y})}^{({t_{a},t_{b}})} = \ ( \frac{{I_{({x,y})}^{t_{a}} - K_{({x,y})}^{t_{b}}}}{I_{({x,y})}^{t_{a}} + K_{({x,y})}^{t_{b}}} )} & (63) \\{K_{({x,y})}^{t_{b}} = {{ERO}_{({x,y})}^{t_{b},d}( {{{{ERO}_{({x,y})}^{t_{b},d} - I_{({x,y})}^{t_{a}}}} < {{{DIL}_{({x,y})}^{t_{b},d} - I_{({x,y})}^{t_{a}}}}} )}} & (64) \\{K_{({x,y})}^{t_{b}} = {{DIL}_{({x,y})}^{t_{b},d}( {{{{DIL}_{({x,y})}^{t_{b},d} - I_{({x,y})}^{t_{a}}}} \leq {{{ERO}_{({x,y})}^{t_{b},d} - I_{({x,y})}^{t_{a}}}}} )}} & (65)\end{matrix}$

Improvement Of SNR

Under typical illumination conditions, the photon shot noise(statistical variation in the arrival rate of incident photons on thesensor) limits the SNR of the detection system. Modern sensors have afull well capacity that is about 1,700 to about 1,900 electrons peractive square micron. Thus, when imaging an object on a plate, theprimary concern is not the number of pixels used to image the object butrather the area covered by the object in the sensor space. Increasingthe area of the sensor improves the SNR for the imaged object.

Image quality may be improved by capturing the image with illuminationconditions under which photon noise governs the SNR (photonnoise=√{square root over (signal)}) without saturating the sensor(maximum number of photons that can be recorded per pixel per frame). Inorder to maximize the SNR, image averaging techniques are commonly used.These techniques are used to address images with significant brightness(or color) differences since the SNR of dark regions is much lower thanthe SNE of bright regions, as shown by the following formula:

$\begin{matrix}{( {{SNR}_{dark} = \frac{SNR_{bright}}{\sqrt{\frac{I_{bright}}{I_{dark}}}}} ).} & (66)\end{matrix}$

In which I is the average current created by the electron stream at thesensor. As colors are perceived due to a difference inabsorption/reflection of matter and light across the electromagneticspectrum, confidence on captured colors will depend upon the system'sability to record intensity with a high SNR. Image sensors (e.g. CCDsensors, CMOS sensors, etc.) are well known to one skilled in the artand are not described in detail herein.

To overcome classical SNR imaging limitations, the imaging system mayconduct analysis of an imaged plate during the image acquisition andadjust the illumination conditions and exposure times in real time basedon the analysis. This process is described in PCT Publication No.WO2015/114121, incorporated by reference, and generally referred to asSupervised High Quality Imaging (SHQI). The system can also customizethe imaging conditions for the various brightness regions of the platewithin the different color channels.

For a given pixel x,y of an image, SNR information of the pixel acquiredduring a current frame N may be combined with SNR information of thesame pixel acquired during previous or subsequent acquired frames (e.g.,N−1, N+1). By example, the combined SNR is dictated by the followingformula:SNR _(x,y,N+1)′=√{square root over (SNR _(x,y,N)′² +SNR _(x,y,N+1)²)}  (67)

After updating the image data with a new acquisition, the acquisitionsystem is able to predict the best next acquisition time that wouldmaximize SNR according to environmental constraints (e.g. minimumrequired SNR per pixel within a region of interest). For example,averaging 5 images captured in non-saturating conditions will boost theSNR of a dark region (10% of max intensity) by √5, when merging theinformation of two images captured in bright and dark conditions optimumillumination will boost the dark regions SNR by √{square root over (11)}in only two acquisitions.

Image Modelling

In some circumstances, when calculating spatial or temporal contrastbetween pixels of one or more images, the pixel information for a givenimage may not be available, or may be degraded. Unavailability mayoccur, for instance, if an image of the plate was not captured withinthe time before bacterial growth (e.g., the plate was not imaged at timet0 or shortly thereafter. Degradation of signal information may occur,for instance, when an image is captured at time t0, but pixels of thecaptured image do not accurately reflect the imaged plate prior tobacterial growth. Such inaccuracies may be caused by temporary artifactsthat do not reappear in subsequent time-series images of the plate(e.g., condensation temporarily forming underneath the plate due tothermal shock when the plate is first put into the incubator).

In such circumstances, the unavailable or degraded image (or certainpixels of the image) may be replaced or enhanced with a model image of aplate. The model image may provide pixel information reflecting how theplate is expected to look at the particular time of the unavailable ordegraded image. In the case of a model image at time t0, the model maybe a plain or standard image of a plate, and may be mathematicallyconstructed using three-dimensional imaging/modelling techniques. Themodel may include each of physical design parameters (e.g., diameter,height, dividers for housing multiple media, plastic material, etc.),media parameters (e.g., type of media, media composition, media heightor thickness, etc.) illumination parameters (e.g., angle of lightsource, color or wavelength(s) of light source, color of background,etc.) and positioning parameters (e.g., position of plate in imagingchamber) in order to produce as real a model as possible.

In the case of a degraded image, enhancement of the degraded image maybe accomplished using signal restoration to sharpen the degraded pixelcharacteristics of the image when the pixel information is not as sharpas the rest of the image (e.g., due to condensation underneath blockingsome light from passing through the plate and therefore making thesection of the plate with condensation slightly less transparent).Signal restoration may involve determining intensity information for therest of the image, identifying a median intensity of the intensityinformation, and then replacing the intensity information for the lesssharp regions of the image with the median intensity of the rest of theimage.

Applications

The present disclosure is based largely on testing performed in salineat various dilutions to simulate typical urine reporting amounts (CFU/mlBucket groups). A suspension for each isolate was adjusted to a 0.5McFarland Standard and used to prepare dilutions at estimated 1×106,1×105, 5×104, 1×104, 1×103, and 1×102 CFU/ml suspension in BD UrineVacutainer tubes (Cat. No. 364951). Specimen tubes were processed usingKiestra InoqulA (WCA1) with the standard urine streak pattern—#4 Zigzag(0.01 ml dispense per plate).

Plates were processed using the ReadA Compact (35 oC, non CO2) andimaged every 2 hours for the first 24 hours and every 6 hours for thesecond 24 hours with a total incubation of 48 hours. Incubation timeswere entered as first reading at 1 hour with allowed margin set as +/−15minutes. For the next reading at 2-24 hours were set for every two hourswith an allowed margin of +/−30 minutes. For reading 24-48 were set forevery 6 hours with allowed margins of +/−30 minutes. After the purefeasibility studies, this was changed to eliminate the allowed margin.This was done to improve image acquisitions at the desired 18 to 24 hourtime range.

In other cases, images could be obtained over a span of 48 hours, at twohour intervals for the first 24 hours and then at 6 hour intervals forthe next 24 hours. In such cases, a total of seventeen images would beobtained in the 48 hour span, including an image obtained from time t0(0 hours).

All acquired images were corrected for lens geometrical and chromaticaberrations, spectrally balanced, with known object pixel size,normalized illumination conditions and high signal to noise ratio perband per pixel. Suitable cameras for use in the methods and systemsdescribed herein are well known to one skilled in the art and notdescribed in detail herein. As an example, using a 4-megapixel camera tocapture a 90 mm plate image should allow enumeration up to 30colonies/mm2 local densities (>105 CFU/plate) when colonies are in therange of 100 μm in diameter with adequate contrast.

The following media were used evaluate the contrast of colonies grownthereon:

TSAII 5% Sheep blood (BAP): a non selection media with worldwide usagefor urine culture.

BAP: used for colony enumeration and presumptive ID based on colonymorphology and hemolysis.

MacConkey II Agar (MAC): a selective media for most common Gram negativeUTI pathogens. MAC is used for differentiation of lactose producingcolonies. MAC also inhibits Proteus swarming. BAP and MAC are commonlyused worldwide for urine culture. Some media are not recommended for usefor colony counting due to partial inhibition of some gram negatives.

Colistin Nalidixic Acid agar (CNA): a selective media for most commonGram positive UTI pathogens. CNA is not as commonly used as MAC forurine culture but helps to identify colonies if over-growth of Gramnegative colonies occurs.

CHROMAgar Orientation (CHROMA): a non-selection media used worldwide forurine culture. CHROMA is used for colony enumeration and ID based oncolony color and morphology. E. coli and Enterococcus are identified bythe media and do not require confirmatory testing. CHROMA is used lessthan BAP due to cost. For mixed samples, CLED media was also used.

Cystine Lactose Electrolyte-Deficient (CLED) Agar: used for colonyenumeration and presumptive ID of urinary pathogens based on lactosefermentation.

The Specimen Processing BD Kiestra™ InoqulA™ was used to automate theprocessing of bacteriology specimens to enable standardization andensure consistent and high quality streaking. The BD Kiestra™ InoqulA™specimen processor uses a magnetic rolling bead technology to streakmedia plates using customizable patterns. The magnetic rolling bead is 5mm in diameter.

FIG. 17 illustrates one example of contrast gathering algorithmperformance in retrieving Morganella morganii growth on both CHROMagar(top images) and blood agar (bottom images) media. CHROMagar and BAP areboth non-selective growing media commonly used in microbiologylaboratories. Each of the middle images of FIG. 17 presents an imagedplate illuminated from a light above the plate (top illumination). Theimages on the left present corresponding spatial contrast of the middleimages. The spatial contrast is based on a 1 millimeter median kernel.Lastly, the images on the right present temporal contrast of the middleimage. The temporal contrast is compiled from images captured between 0hours (t0) and 12 hours (tx) of incubation using various lightingconditions (different color channels, illumination settings, etc.) ateach image capture time.

As shown in FIG. 17, local contrast has its limitations when dealingwith semitransparent colonies, especially where edge transitions aresmall, as only the most contrasted confluence regions can be picked outfrom the image using the spatial contrast algorithm alone. the spatialcontrast can fail to pick up the object. Also evident from FIG. 17 isthe efficiency of temporal contrast on isolated colonies.

FIG. 17 (particularly the blood agar images on the bottom) alsohighlights the issues of using spatial contrast alone to detect growthon transparent media. The ink printed on the transparent case has verystrong edges, and thus strong spatial contrast. This ends up making anyother spatial contrast very difficult to see, since the colonies do nothave as strongly defined edges. Thus, the use of temporal contrast inconjunction with spatial contrast is of considerable benefit in thepresent disclosure.

Ultimately, the result of the above described contrast determinations isthat methods for rapid detection and identification of colonies in animaged media can be automated. The automated methods provide significantadvantages over comparable manual methods.

FIG. 18 shows a pair of flow charts comparing a timeline of an automatedtest process 1800 (e.g., the routine 200 of FIG. 2) to a timeline of acomparable manually-performed test process 1805. Each process beginswith the specimen for testing being received at a laboratory 1810, 1815.Each process then proceeds with incubation 1820, 1825, during which thespecimen may be imaged several time. In the automated process, anautomated evaluation 1830 is made after approximately 12 hours ofincubation, after which time it can be definitively determined whetherthere is no growth (or normal growth) in the specimen 1840. As shownfrom the results in FIG. 17, the use of temporal contrast in theautomated process greatly improves the ability to detect colonies, evenafter only 12 hours. By contrast, in the manual process, a manualevaluation 1835 cannot be made until nearly 24 hours into the incubationprocess. Only after 24 hours can it be definitively determined whetherthere is no growth (or normal growth) in the specimen 1845.

The use of an automated process also allows for faster AST and MALDItesting. Such testing 1850 in an automated process can begin soon afterthe initial evaluation 1830, and the results can be obtained 1860 andreported 1875 by the 24 hour mark. By contrast, such testing 1855 in amanual process often does not begin until close to the 36 hour mark, andtakes an additional 8 to 12 hours to complete before the data can bereviewed 1865 and reported 1875.

Altogether, the manual test process 1805 is shown to take up to 48hours, requires a 18-24 hour incubation period, only after which is theplate evaluated for growth, and further has no way to keep track of howlong a sample has been in incubation. By contrast, because the automatedtest process 1800 can detect even relatively poor contrast betweencolonies (compared to background and each other), and can conductimaging and incubation without a microbiologist having to keep track oftiming, only 12-18 hours of incubation is necessary before the specimencan be identified and prepared for further testing (e.g., AST, MALDI),and the entire process can be completed within about 24 hours. Thus, theautomated process of the present disclosure, aided with the contrastprocessing described herein, provides faster testing of samples withoutadversely affecting the quality or accuracy of the test results.

Although the invention herein has been described with reference toparticular embodiments, it is to be understood that these embodimentsare merely illustrative of the principles and applications of thepresent invention. It is therefore to be understood that numerousmodifications may be made to the illustrative embodiments and that otherarrangements may be devised without departing from the spirit and scopeof the present invention as defined by the appended claims.

The invention claimed is:
 1. A method for evaluating microbial growth onplated media that has been inoculated with a culture and incubated, themethod comprising: obtaining first and second digital images of theplated media, each digital image obtained after a period of incubationfor the inoculated media and at a different time; identifying one ormore static features in the first and second digital images; aligningthe second digital image with the first digital image, wherein saidalignment is based on coordinates assigned to pixels of the firstdigital image and one or more pixels of the second digital imagecorresponding to the pixels of the first digital image that wereassigned to coordinates; generating temporal contrast data indicative ofchanges between corresponding pixels of the first and second digitalimages; identifying an object in the second digital image from thetemporal contrast data; using a classifier to classify the identifiedobject as a type of organism based on one or more dynamic objectfeatures of the identified object; and storing the identified object andits classification in memory.
 2. The method of claim 1, wherein the oneor more static features includes at least one of: polar coordinates ofthe object; a vector of the polar coordinates; morphometric features;contextual features; spectral features; or background features.
 3. Themethod of claim 1, further comprising: obtaining a third digital imageof the plated media after the period of incubation for the inoculatedmedia and at a different time than when the first and second digitalimages were obtained; aligning the third digital image with the firstand second digital images; and generating another temporal contrast dataindicative of changes between corresponding pixels of the second andthird digital images, wherein the object is identified based further onthe another temporal contrast data, and wherein the one or more dynamicobject features of the identified object includes a second derivative ofthe another temporal contrast data.
 4. The method of claim 1, whereinthe one or more dynamic object features of the identified objectincludes an object growth acceleration rate.
 5. The method of claim 1,wherein the one or more dynamic object features of the identified objectincludes at least one of: a growth rate of the identified object; achange to a chromatic feature of the identified object; or a change ingrowth along an axis proximately normal to the plated media.